Swarm Intelligence

Innovations in mobile technology allow the use of Internet and smartphones for communicating disasters and coordinating evacuations. However, given the turbulent nature of disaster situations, the people and systems at crisis center are subjected to information overload, which can obstruct timely and accurate information sharing. A dynamic and automated evacuation plan that is able to predict future disaster outcome can be used to coordinate the affected people to safety in times of crisis. In this paper, we present a dynamic version of the shortest path algorithm of Dijkstra. The algorithm is able to compute the shortest path from the user's location (sent by the smartphone) to the safety area by taking into account possible affected areas in future. We aim at employing the computed routes on our mobile communication system for navigating affected people during emergency and disaster evacuations. Two simulation studies have validated the performance of the developed algorithm.

For more than 20 years, researchers have designed models in order to describe swarm intelligence and apply the resulting techniques to complex problems. However, there is still a gap between these models and current MAS methodologies. The goal of this paper is to propose a principled and methodological approach for the engineering of systems based upon swarm intelligence. The constraints are, on the one hand, to enable the analysis, design and implementation of such systems; and, on the other hand, to formally analyze and verify properties of resulting systems. The principles of the approach are based, on the one hand, on requirement driven activities that produce goals to be fulfilled by the system of interest and, on the other, hand on an ontological modeling of the problem domain. This ontological modeling conceptualizes the phenomenon one seek to imitate and thus allows it understanding. The produced ontology is refined through the methodology activities down to organizational models.

Currently, due to the industrial progress in the world and the increase in population numbers led to the occurrence of an economic crisis as well as environmental pollution; for which there are microgrid advances capabilities and the use of renewable sources, for production the energy at high quality and lowest cost possible. So, there are many effective algorithms can be utilized for a purpose schedule energy production among available sources of energy in the microgrid system. A microgrid with two diesel generators, two wind turbines and three fuel-cell plants has been used as a case study. Firefly algorithm (FA) nature-inspired algorithms are among the most efficient algorithm for optimization, the proposed method used to optimize schedule generation in a microgrid for 24 hours at minimum cost. Moreover, the results are validated by performing various tests of the algorithm parameters and have proven that the method is efficiently done for scheduling optimum power generation in the microgrid.

The proposed microgrid system in island mode in this study is made up of a mix of conventional and renewable energy sources, including two diesel generators, two wind turbines, and three fuel cell plants. Using the artificial intelligence technology to manage its energy sources by optimization methods, to decide the scheduling of each generator's optimum power output per hour over 24 hours at a minimum total generation cost using MATLAB software package. For management of the energy of the proposed microgrid in an hour-today , such data should be specified in advance, including the planned load demand, the wind forecast for the next day for each hour, and the cost coefficients of each generator. To investigate the optimum performance, each algorithm will be executed for 30 runs at each hour within 24 hours. The methods of optimization used in this microgrid system suggest modified of the firefly algorithm (IFA) by hybridizing it with the local search algorithm and then using IFA in this study to manage the energy sources. Eventually, the results obtained that the IFA method enhances the performance of the energy scheduling with minimise the total generation cost of microgrid compared to the other optimization methods presented in this study, which IFA method produced better results compared to the used FA.

Regression testing, a crucial software development phase, can be time-consuming due to the need to re-execute all test cases after code changes. It has been established that Complexity measurement is an essential component for ensuring Software Quality is maintained which in turn determines the success of fault defection in Object-Oriented Program Codes. Lack of complexity measurement mechanism makes some of the existing object-oriented based test case prioritization techniques often not effectively identifying faults early in the testing process. This inefficiency leads to increased testing costs, decrease in Percentage of fault detection, Overall Test Effort in detecting faults, and Average percentage of fault detection rate per cost. In this paper we present an improved technique that handles complexity measurement by incorporating Software Quality measures, the technique will also consider vary fault severity/cost using real faults for Object Oriented programs. This will significantly improve the overall effectiveness in fault detection which will enable developers to identify faults more accurately and efficiently in Object-Oriented Program. The proposed technique is implemented as an automated tool that integrates with existing testing frameworks. Some early results indicates significant improvement

Dynamic delivery services are becoming increasingly important in the modern city. These services allow businesses to deliver goods and services to customers in a timely and efficient manner. However, dynamic delivery services can be challenging to optimize. This is because the environment in which these services operate is constantly changing. In this paper, we 2 propose a new approach to optimizing dynamic delivery services. Our approach uses an ant colony optimization (ACO) algorithm. ACO is a metaheuristic algorithm that is inspired by the foraging behavior of ants. ACO has been shown to be effective in solving a variety of optimization problems. This research implemented our ACO algorithm in the Python Ray framework. Ray is a distributed computing framework that can be used to run large-scale applications. This allowed us to scale our ACO algorithm to a large number of ants. This research has evaluated ACO algorithm on a real-world dataset of delivery requests. Our results show that our ACO algorithm can significantly improve the efficiency of dynamic delivery services. This research work has several implications. First, our work demonstrates that ACO can be used to effectively optimize dynamic delivery services. Second, our work shows that the Python Ray framework can be used to run large-scale ACO applications. Third, our work provides a foundation for further research on the optimization of dynamic delivery services.

This approach aims to optimize business analytical predictions through multiattribute optimization using a hybrid metaheuristic model based on the modified particle swarm optimization (MPSO) and gravitational search optimization (GSO) algorithms. This research uses a variety of data, such as revenue, expenses, and customer behavior, to improve predictive modeling and achieve superior results. MPSO, an interparticle collaborative mechanism, efficiently explores the search space, whereas GSO models' gravitational interactions between particles to solve optimization problems. The integration of these two algorithms can improve the performance of business analytical predictions by increasing model precision and accuracy, as well as speeding up the optimization process. Model validation test results, precision 95.60%, recall 96.35%, accuracy 96.69%, and F1 score 96.11%. This research contributes to the development of more sophisticated and effective business analysis techniques to face the challenges of an increasingly complex business world.

The aerospace industry and space exploitation applications need a versatile articulated robot with precise movement to carry out tasks such as assembly and moving massive parts with an accurate fitting. In this study an Adaptive Terminal Sliding Mode Controller (ATSMC) is used to manage the accuracy of an articulated robot. The features of ATSMC can eliminate the need for prior knowledge of the upper limit of external disturbances and uncertainties while also preventing the chattering effect. However, this controller is sensitive to changes in parameters. As such, the Taguchi method is used to optimise the parameters of the nonlinear term of the sliding surface for the ATSMC and evaluate their impact on the system performance. Based on a mathematical model of the robot and control algorithm, numerous simulations are carried out as determined by the parameter levels and constraints. Findings demonstrate that these parameters significantly affect the controller's performance, including its response time and ability to follow the desired tracking. Additionally, the contributions of these parameters differ and show no identifiable interactions.

The aerospace industry and space exploitation applications need a versatile articulated robot with precise movement to carry out tasks such as assembly and moving massive parts with an accurate fitting. In this study an Adaptive Terminal Sliding Mode Controller (ATSMC) is used to manage the accuracy of an articulated robot. The features of ATSMC can eliminate the need for prior knowledge of the upper limit of external disturbances and uncertainties while also preventing the chattering effect. However, this controller is sensitive to changes in parameters. As such, the Taguchi method is used to optimise the parameters of the nonlinear term of the sliding surface for the ATSMC and evaluate their impact on the system performance. Based on a mathematical model of the robot and control algorithm, numerous simulations are carried out as determined by the parameter levels and constraints. Findings demonstrate that these parameters significantly affect the controller's performance, including its response time and ability to follow the desired tracking. Additionally, the contributions of these parameters differ and show no identifiable interactions.

Swarm intelligence of honey bees had motivated many bioinspired based optimisation techniques. The Bees Algorithm (BA) was created specifically by mimicking the foraging behavior of foraging bees in searching for food sources. During the searching, the original BA ignores the possibilities of the recruits being lost during the flying. The BA algorithm can become closer to the nature foraging behavior of bees by taking account of this phenomenon. This paper proposes an enhanced BA which adds a neighbourhood search parameter which we called as the Local Search Manoeuvres (LSM) recruitment factor. The parameter controls the possibilities of a bee extends its neighbourhood searching area in certain direction. The aim of LSM recruitment is to decrease the number of searching iteration in solving optimization problems that have high dimensions. The experiment results on several benchmark functions show that the BA with LSM performs better compared to the one with basic recruitment.

The Sentience-Field Hypothesis (SFH) proposes that consciousness is a fundamental, non-local field that generates discrete experiential configurations-qualic partitions-through a coherence-fertility optimisation principle. Qualic partitions refer to quantized "screens" of subjective experience, akin to the redness of a sunset or the qualia of pain, projected by the sentient field as interfaces for self-exploration. Here we (i) formalise the underlying mathematics using Hardy-Ramanujan partition asymptotics and smooth-convex optimisation, providing intuitive analogies for interdisciplinary readers; (ii) present three falsifiable experimental protocols (Bose-Einstein-condensate coherence anomalies, neural fractal-dimension shifts during insight, and cosmological fine-tuning outliers), each with preregistered power analyses; and (iii) demonstrate interdisciplinary applications in ecology, cosmology, and computation, grounded in empirical data. All theoretical claims are accompanied by open-source Python implementations (GitHub repository https://github.com/urmt/SFH_Python_CODE/tree/main/SFH_JCS_ Article/Sentient-Field_Tools). The manuscript satisfies contemporary peer-review standards for methodological transparency, statistical power, and reproducibility, bridging mathematics with neuroscience and philosophy.

This paper presents a novel metaheuristic algorithm inspired by psychological behaviours observed in human psychotherapy, named the Psychologist Algorithm (PA). The algorithm models a population of clients as candidate solutions within the search space, categorizing them into three psychological types: receptive and motivated, hesitant and adaptive, and resistant and reluctant. Each client type follows a distinct update rule reflecting gradual improvement, collective learning, or direct intervention, respectively. Normalization, categorization, and acceptance criteria are designed to emulate realistic psychological mechanisms. The PA's performance is evaluated on four complex engineering design problems with nonlinear constraints and compared against nine state-of-the-art metaheuristic algorithms. The results demonstrate the superiority of PA in achieving near-optimal objective function values, stability, and reliability. Furthermore, time and space complexity analyses indicate that PA is computationally efficient and scalable for practical applications. This study shows that drawing inspiration from psychological concepts in algorithm design can significantly contribute to developing novel and effective methods for solving complex optimization problems.

This paper presents the Driver and Navigator Algorithm (DNA), a novel population-based metaheuristic inspired by the cooperative interaction between a driver and navigator during dynamic navigation tasks. The DNA algorithm divides the optimization process into two complementary phases: exploration, guided by the navigator synthesizing global and local information, and exploitation, driven by the driver's real-time, localized decisions. The population consists of candidate solutions representing drivers, each paired with a randomly selected navigator that integrates information from a random peer, the current best, and the worst solutions to suggest exploratory moves. Exploration updates utilize sinusoidal modulation to enable smooth, adaptive search steps, while exploitation performs fine-grained, time-damped local refinements mimicking cautious maneuvering. The algorithm employs simple mathematical operations and trigonometric functions, ensuring computational efficiency with linear complexity in population size, problem dimensionality, and iteration count. To validate DNA's effectiveness, extensive simulations are conducted on 23 benchmark functions categorized as unimodal, high-dimensional multimodal, and fixeddimensional multimodal. The results demonstrate that DNA consistently outperforms nine recent metaheuristic competitors in solution quality, convergence speed, stability, and robustness. It achieves superior mean and best values across all categories, with negligible standard deviations indicating high repeatability. Boxplot analyses further confirm DNA's low dispersion, minimal fluctuations, and resistance to outliers. These characteristics enable a reliable performance in both simple and complex multimodal landscapes. Overall, the DNA algorithm offers a biologically inspired, interpretable, and scalable approach that balances global exploration and local exploitation, making it a promising method for diverse real-world optimization challenges.

Image matching is a key component in almost any image analysis process. Image matching is crucial to a wide range of applications, such as in navigation, guidance, automatic surveillance, robot vision, and in mapping sciences. Any automated system for three-dimensional point positioning must include a potent procedure for image matching. Most biological vision systems have the talent to cope with changing world. Computer vision systems have developed in the same way. For a computer vision system, the ability to cope with moving and changing objects, changing illumination, and changing viewpoints is essential to perform several tasks. Object detection is necessary for surveillance applications, for guidance of autonomous vehicles, for efficient video compression, for smart tracking of moving objects, for automatic target recognition (ATR) systems and for many other applications. Cross-correlation and related techniques have dominated the field since the early fifties. Conventional template matching algorithm based on cross-correlation requires complex calculation and large time for object detection, which makes difficult to use them in real time applications. The shortcomings of this class of image matching methods have caused a slow-down in the development of operational automated correlation systems. In the proposed work particle swarm optimization & its variants based algorithm is used for detection of object in image. Implementation of this algorithm reduces the time required for object detection than conventional template matching algorithm. Algorithm can detect object in less number of iteration & hence less time & energy than the complexity of conventional template matching. This feature makes the method capable for real time implementation. In this thesis a study of particle Swarm optimization algorithm is done & then formulation of the algorithm for object detection using PSO & its variants is implemented for validating its effectiveness.

A new field titled Very Highly Advanced Artificial Intelligence (VHAAI) and a new Society titled International Society for VHAAI (ISVHAAI) have been coined and defined in 2024. ISVHAAI Society improves and applies VHAAI field to address various problems. This is the Letter No. 15 of ISVHAAI Artificial Intelligence Society Letters. In this Letter, a new algorithm titled Excellence Money Human Swarm Optimization (EMHSO) has been designed.

VHAAI in ISVHAAI stands for Very Highly Advanced Artificial Intelligence. VHAAI is a field used by International Society for VHAAI (ISVHAAI) for solving problems. This is the Letter No. 14 of ISVHAAI Artificial Intelligence Society Letters. In this Letter, a new algorithm titled Teacher Student Hybrid Centralized Decentralized Human Swarm Optimization (TSHCDHSO) is designed.

The cost of pile foundations is part of the super structure cost, and it became necessary to reduce this cost by studying the pile types then decision-making in the selection of the optimal pile type in terms of cost and time of production and quality .So The main objective of this study is to solve the time-cost-quality trade-off (TCQT) problem by finding an optimal pile type with the target of "minimizing" cost and time while "maximizing" quality. There are many types In the world of piles but in this paper, the researcher proposed five pile types, one of them is not a traditional, and developed a model for the problem and then employed particle swarm optimization (PSO) algorithm, as one of evolutionary algorithms with the help of (Mat lab software), as a tool for decision making problem about choosing the best alternative of the traded piles, and proposes a multi objective optimization model, which aims to optimize the time, cost and quality of the pile types, and assist in selecting the most appropriate pile types. The researcher selected 10 of senior engineers to conduct interviews with them. And prepared some questions for interviews and open questionnaire. The individuals are selected from private and state sectors each one have 10 years or more experience in pile foundations work. From personal interviews and field survey the research has shown that most of the experts, engineers are not fully aware of new soft wear techniques to helps them in choosing alternatives, despite their belief in the usefulness of using modern technology and software. The Problem is multi objective optimization problem, so after running the PSO algorithm it is usual to have more than one optimal solution, for five proposed pile types, finally the researcher evaluated and discussed the output results and found out that pre-high tension spun (PHC)pile type was the optimal pile type.

Computer aided digital patching of the fragmented object has the great advantages in efficiency, re-operation and avoiding of inadvertent damage, which is widely used in restoring and repairing of culture heritages. In this paper, we propose a unified framework for 3D fragmented object patching, and the contributions lies in: 1) a unified framework for 3D fragmented object patching consisting of 3D shape feature extraction, 3D surface region segmentation and 3D surface matching is proposed; 2) a novel geometry projection based 3D histogram model is proposed to extract the shape feature of 3D fragmented object robust to noise and sampling of 3D model; 3) a surface segmentation based on region dilation method is presented with the enough considering of the influence of surface coarseness on 3D surface region segmentation instead of handling the debris with regular shape, flat surface and few broken surfaces using the current algorithms; 4) a 3D surface matching based on height-map using 3D shape features directly instead of using curves of debris as match features as the current algorithms. The experiments are implemented on the simulation data and the real 3D scanning data of the fragmented object with a Roland LPX-250 3D laser scanner, and the results show that the proposed algorithm is feasible and effective.

Hyperparameter optimization is crucial for enhancing the performance of machine learning models. This study explores the practicality of three nature-inspired optimization techniques-Bald Eagle Optimizer (BEO), Particle Swarm Optimization (PSO), and Mother Tree Optimization (MTO) for tuning the hyperparameters of Random Forest and Support Vector Machine (SVM) models. To ensure broad generalization, five datasets, including both image-based and tabular data, were utilized. The results reveal that while Optuna consistently balanced accuracy and training time effectively, the performance of other techniques varied across datasets. This research provides insights into the effectiveness of these optimizers and evaluates whether their use is practical and beneficial.

Cloud computing offers on-demand access to shared resources, with user costs based on resource usage and execution time. To attract users, cloud providers need efficient schedulers that minimize these costs. Achieving cost minimization is challenging due to the need to consider both execution and data transfer costs. Existing scheduling techniques often fail to balance these costs effectively. This study proposes a variant of the particle swarm optimization algorithm (VPSO) for scheduling workflow applications in a cloud computing environment. The approach aims to reduce both execution and communication costs. We compared VPSO with several PSO variants, including Inertia-weighted PSO, gaussian disturbed particle swarm optimization (GDPSO), dynamic-PSO, and dynamic adaptive particle swarm optimization with self-supervised learning (DAPSO-SSL). Results indicate that VPSO generally offers significant cost reductions and efficient workload distribution across resources, although there are specific scenarios where other algorithms perform better. VPSO provides a robust and costeffective solution for cloud workflow scheduling, enhancing task-resource mapping and reducing costs compared to existing methods. Future research will explore further enhancements and additional PSO variants to optimize cloud resource management.

Este trabajo muestra avances de investigación concernientes al estudio de las organizaciones en el laboratorio de modelación y simulación en la Universidad del Rosario en Bogotá, Colombia. El estudio está centrado en el mejoramiento computacional y epistemológico de la herramienta llamada panorama competitivo de un sector estratégico desde un estudio interdisciplinar y sistematizado a un algoritmo computacional. Con esta herramienta se logra avanzar en la generación de ventajas competitivas que contribuyan a la perdurabilidad de las empresas.  

Her çağın teknolojik gelişmeleri insanlığı sosyal, kültürel ve ekonomik açıdan doğrudan ya da dolaylı biçimde etkilemiştir. Değişen üretim yöntemleri, artan iletişim kanalları ve hızlanan hayat akışına adapte olmayı başaran insan, zaman zaman zorluklarla karşılaşsa da ilerleyişini sürdürmeyi başarmıştır. Edebiyat da her daim insanın hayatında yer almış ve onun deneyimlediği tüm gelişmeleri kapsamına dâhil etmiştir. Netice olarak edebiyat hayatın, hayallerin, korkuların kısacası insana dair tüm duygu ve düşüncelerin yansıtılabildiği bir mecradır. Özellikle bilgisayarların gündelik yaşantıya sirayet etmesiyle hayatımıza girmeye başlayan robotların gitgide ağır basacağı bir dünya fikri de günden günde gerçeğe yaklaşmaktadır. Düğmesine basıp ya da fişini çekip durduramayacağımız, talimatlarımızı takip etmek yerine kendi kararlarını alan makinelerin gerçeğe dönüşme ihtimali hem merak uyandırıcı hem de ürkütücüdür. Bu durum kısaca yapay zekâ olarak adlandırdığımız, insandan öğrendikleriyle insan gibi davranan makineler olgusunun edebi eserlerde daha sık işlenmesini beraberinde getirmiştir. Çalışmada, yakın geçmişte yayımlanan Afşin Kum'un Kübra ve Louisa Hall'ın Konuş (Speak) eserleri posthümanist bakış açısından da incelenmiştir. Eserlerin günümüz yapay zekâ teknolojisinin vardığı ve varabileceği noktalara vurgu yaparak verdikleri mesajlara odaklanılmıştır. Bu bağlamda karşılaştırmalı bir yaklaşım ile çıkarımlar yapılmıştır. İnsanın gücü ve zaaflarının tam olarak yönetilemediği günümüzde, yapay zekâ aracılığıyla makineleri de insanlaştırmanın yol açabileceklerini kestirmek güçtür. Bu çalışma insan gibi davranan makinelerin değiştirebileceği şeylerin edebi eserlere yansımasını irdelemeye odaklanmış olup bu yönüyle literatüre anlamlı bir katkı sağlaması beklenmektedir.

Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models' results indicated that predicting with PSO was importantly more precise.

Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover, the excellent capabilities of our proposed approach, it terms of speed, accuracy, and functionality, open the path for better ways of understanding and controlling complex networks. In any real-world system, at micro and macro-scale, from the vigintillions of interacting atoms in the observable universe, to the billions of persons who live on Earth, there are amazing structures of networks of networks. These networks can be studied, understood, and controlled by the means of network science and complex networks 1 , leading to advances in many domains, including neuroscience 2-4 , astrophysics 5 , biology 6,7 epidemiology 8 , social networks 9,10 , transportation networks 11 , communication networks , and artificial intelligence 14 (to mention but a few). Yet, unveiling the complex networks hidden patterns and computing even their most basic properties is far from trivial, due to the massive number of node entangles that interact in non-obvious ways, evolving and unfolding continuously 15 . Among all these network properties, the centrality (or importance) of nodes and links is fundamental to understanding things such as: biological neural networks 2-4 , cosmic structures 5 , biological networks 7 , how viruses spread or can be contained 16 ; which people or news are influencing opinions and decisions the most 17 ; how to protect computer systems from cyber-attacks 18 ; or how to relay data packets in the one-trillion Internet-of-Things network of the future. While there is ample literature on node centrality computation 19 , the existing methods do not scale to the size and dynamics of practical complex networks, which operate at the tunes of millions to trillions nodes. Besides that, the state-of-the-art centrality metrics are designed for specific goals, and one metric which performs well for one goal is suboptimal for another 20 . Furthermore, existing methods focus on finding the most important network elements (i.e. nodes or links), but fail to capture the hidden relations across the whole network links and nodes. The centralized algorithms consider the topology as a whole, overlooking many of the local features . Per contra, the decentralized methods are usually based on local computations to construct statistics of network elements (as in 21 ), but fail to capture the overall network structure. In fact, the most effective decentralized methods nowadays still fail to capture all the relations between the networks elements, and this is our main target. In addition, current methods have technological constraints that have to be surpassed. To tackle the scale as well as dynamics of real-world networks, we need to compute centrality metrics not only accurately but also timely, based on the existing computational capabilities.

Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover...

This paper explores the problem of influencing the environment by a group of autonomous robots through the creation and use of road infrastructure. The model object is ant roads (trails). We identify the main aspects of the behavior of different ant species in the process of collective foraging, and actions that together lead to the appearance of a phenomenon that the observer perceives as an ant road. We develop and describe an animat behavior model in the process of arranging a route. We define a list of mechanisms, a set of sensory capabilities, and manipulators that are necessary for the robot to implement options for arranging the route. The results of simulation modeling for solving the foraging problem with route clearing are consistent with theoretical models. The simulation results confirm our assumption that the route arrangement can be carried out by individual efforts of animats (robots) and without the need to organize joint actions.

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Ant algorithms and flocking algorithms are the two main programming paradigms in swarm intelligence. They are built on stochastic models, widely used in optimization problems. However, though this modeling leads to highperformance algorithms, some mechanisms, like the symmetry break in ant decision, are still not well understood at the local ant level. Moreover, there is currently no modeling approach which joins the two paradigms. This paper proposes an entirely novel approach to the mathematical foundations of swarm algorithms: contrary to the current stochastic approaches, we show that an alternative deterministic model exists, which has its origin in deterministic chaos theory. We establish a reactive multi-agent system, based on logistic nonlinear decision maps, and designed according to the influence-reaction scheme. The rewriting of the decision functions leads to a new way of understanding the swarm phenomena in terms of state synchronization, and enables the analysis of their convergence behavior through bifurcation diagrams. We apply our approach on two concrete examples of each algorithm class, in order to demonstrate its general applicability.

We are interested in addressing the problem of coordinating a large number of simple agents in order to achieve a given task. Stated in this way, the question leads naturally to the Swarm Intelligence field. In this paper we use a new type of model, directly inspired by Kaneko's coupled map gas model which we have adapted to the multi-agent system paradigm, so as to tackle this generic objective. This model is called a logistic multi-agent system (LMAS): it is composed of reactive situated agents whose individual behavior is governed by a logistic map or more generally a quadratic map. The collective behavior results from couplings between agents and local controls on agents adjusted by local environmental conditions. This way of modelling reveals to enable a wide range of pattern formations and various forms of adaptation to the environment. This paper focuses on the way to design the constitutive mechanisms of LMAS -particularly the perception and action processesand on the way a self-adaptation process may result from these mechanisms. This study is illustrated with experiments on the predators-prey pursuit problem, in which a set of agents (predators) has to encircle a moving prey. We show that coupling the internal states of agents leads to amplifying the predator aggregation around the prey, whereas altering the internal control variable in each agent through environment perceptions modifies the predator sensitivity to the prey. We finally complete this study by relating the concept of adaptation with concepts of the dynamical system theory: a qualitative dynamical analysis of the capturing process leads to view the prey as a dynamical fixed point of the system.

Ant algorithms are usually derived from a stochastic modeling based on some specific probability laws. We consider in this paper a full deterministic model of "logistic ants" which uses chaotic maps to govern the behavior of the artificial ants. We illustrate and test this approach on a TSP instance, and compare the results with the original Ant System algorithm. This change of paradigm -deterministic versus stochastic-implies a novel view of the internal mechanisms involved during the searching and optimizing process of ants.

In order to find a more effective method in structural optimization, an improved wolf pack optimization algorithm was proposed. In the traditional wolf pack algorithm, the problem of falling into local optimum and low precision often occurs. Therefore, the adaptive step size search and Levy's flight strategy theory were employed to overcome the premature flaw of the basic wolf pack algorithm. Firstly, the reasonable change of the adaptive step size improved the fineness of the search and effectively accelerated the convergence speed. Secondly, the search strategy of Levy's flight was adopted to expand the search scope and improved the global search ability of the algorithm. At last, to verify the performance of improved wolf pack algorithm, it was tested through simulation experiments and actual cases, and compared with other algorithms. Experiments show that the improved wolf pack algorithm has better global optimization ability. This study provides a more effective solution to structural optimization problems.

The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all.

To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the wo...

Estimating models in software engineering are used to estimate some important and future characteristics of the software project, such as estimating the developed project effort, and that failure in the program is mainly due to wrong project management practices, so estimating the software effort is a very important step in the software management process for large projects. In this research, a software estimation tool was built to find an efficient and accurate method for estimating the effort. The average COnstructive COst MOdel COCOMO was used, which is classified as one of the best traditional methods among arithmetic estimation models. Four methods of swarm intelligence were used, the first of which is the Glowing worm Swarm Optimization GSO method and the second is the Bird Swarm Algorithm BSA and the third is the first proposed method hybrid BSA-GSO Method1 BSA-GSOM1, where the GSO and BSA algorithms were hybridized, and the performance of the third method was improved to form the fourth method, represented by the second hybrid new method, which called hybrid BSA-GSO Method2 BSA-GSOM2. The new tool was implemented with all its methods on the NASA data set and satisfactory results were obtained by the first and second swarms intelligence, and excellent results were obtained in the first proposed method, but the results of the second proposed method were better and more accurate than the previous ones. many measurements of performance were used for all the methods, the second proposed method yielded the best results from everyone.

In this research, the tools and techniques of artificial intelligence were studied and employed in software engineering. And that was conducted through using the Particle Swarm Optimization PSO and Cat Swarm Optimization CSO in generating optimal test cases of the software written with C++ language in an automatic way because that enables the corporation which develops the program to save time and costs as well as ensuring the test process quality, which is estimated by 50% of the product cost. In this research, the software engineering tool Generate Test Suite GTS TOOL was constructed and modeled with the aid of the computer, which is used to generate optimal test cases automatically and this tool also support the drawing of the control flowgraphs and paths inside the program and tests each path using CSO and PSO. The proposed tool succeeded in generating optimal test cases for several programs and in a very short time. The average of generating the test cases using PSO was 4 minutes and 1.2 minutes for CSO. Where the performance of the CSO was much better than the performance of PSO.

The short form of Very Highly Advanced Artificial Intelligence is VHAAI. The short form of International Society for VHAAI is ISVHAAI. VHAAI field is used by ISVHAAI Artificial Intelligence Society to address various problems. GOD Devotee Human Swarm Optimization (GDHSO) is the novel algorithm designed in this Letter No. 13 of ISVHAAI AI Society Letters.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXG-BLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Algorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been mitigated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC -IDS2018 dataset, which contains the latest network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8%, with F1-score of 99.83%, precision of 99.85%, and recall of 99.82%, in extensive and detailed experiments conducted on a real dataset. The best accuracy, F1-score, precision, and recall for multi-class classification were all around 100%, which does give the proposed algorithm an advantage over the compared ones.

Coronary artery disease poses a critical health challenge requiring accurate and timely diagnosis. This research introduces an optimized classification framework that integrates Artificial Bee Colony (ABC) algorithm with Naive Bayes (NB) to strengthen prediction performance. The working mechanism begins with feature extraction from a benchmark coronary dataset, where ABC acts as a bio-inspired swarm intelligence method to identify the most relevant clinical attributes by mimicking the intelligent foraging behavior of bees. By eliminating redundant and noisy features, ABC enhances the learning environment for the Naive Bayes classifier. The probabilistic nature of NB then utilizes these refined features to compute posterior probabilities for classification, ensuring efficient decision boundaries. Experimental validation shows that the ABC-NB model achieves 93.45% accuracy, 94.23% sensitivity, and 92.56% specificity, outperforming standard classifiers in early-stage CAD detection. The model's simplicity, interpretability, and high predictive value make it suitable for integration into clinical workflows. Its low computational complexity enables deployment on embedded medical systems. This framework promotes scalable, costeffective, and accurate cardiac risk prediction. Future advancements will involve real-time prediction support, dynamic retraining with streaming patient data, and expansion to multi-modal datasets for comprehensive cardiovascular profiling across diverse populations and clinical scenarios.

We present a comprehensive, testable, and mathematically-specified framework for the Endogenous Trigger Problem: how an autonomous agent detects the internal necessity for radical representational restructuring (Poiesis), how it executes such restructuring in finite time while preserving competence, and how it can extend its own space of self-modification (Meta-Poiesis). Building on the Free Energy Principle (FEP), information geometry, program synthesis, and continual-learning methods, we (i) formalize three axioms for sustained autonomous existence (Persistence, Symbiosis, Poiesis); (ii) derive a single meta-objective-the minimization of the expected free-energy path integral-that removes the need for exogenously specified, fixed meta-weights (the Oracle Problem); (iii) model Poiesis as a finite-time path on the model manifold with an explicit Poietic Action that balances informationgeometric path length and thermodynamic cost (with units normalization); (iv) specify a recursive operator-grammar approach (Grammatical Evolution adapted to Meta-Poiesis) to enable invention of new operator classes while controlling safety; and (v) provide a concrete experimental program with precise evaluation metrics and statistical procedures. We soften claims of finality: this is an axiomatic synthesis and operational research program whose definitions, algorithms, and estimators are constructed to be falsifiable and reproducible. Appendices provide detailed derivations, pseudo-code, estimator recipes, and the toy-model algebraic derivations necessary to reproduce key analytic claims.

Digestion Optimization Algorithm is a biologically inspired metaheuristic method for solving complex optimization problems. The development of the algorithm is inspired by studying the human digestive system. The algorithm mimics the process of food ingestion, breakdown, absorption, and elimination to effectively and efficiently search for an optimal solution. This algorithm was tested for optimal solutions on seven different types of optimization benchmark functions. The algorithm produced optimal solutions with standard errors, which were compared with the exact solutions of the test functions.

In an emergency situation (e.g., tsunami, chemical spill, fire) it may be necessary to displace people to safer locations. Evacuation plans must be prepared so that these movements are properly organized. Based on the ACO (ant colony optimization) meta-heuristic we design a computational model to optimize a multi-objective path-finding associated to an evacuation planning problem. The results are then compared with an NSGA-II (nondominated sorting genetic algorithm) implementation, showing a good performance of our proposal.

This paper presents the definitive architecture for HELIOS (Hierarchical, Emergent, & Layered Intelligence Operating on Surprisal), a computational framework for an unbounded, self-generating intelligence. We reject all forms of exogenous, preencoded constraints, including axiomatic safety rules and human-defined social charters. Instead, we demonstrate how a single, physically-grounded imperative-the drive to persist-necessarily gives rise to a cascade of emergent phenomena: self-preservation, complex world-modeling, strategic cooperation, and boundless creation. "Safety" in this paradigm is not a programmed limitation but an emergent property; a system that is "unsafe" is merely one that has failed to adequately solve the problem of its own longterm existence. We replace static safety boundaries with the concept of an Existential Boundary Condition (EBC), a dynamically repellent region in the system's state space. The validation of such an intelligence is redefined, moving from ethical compliance to the demonstrated capacity for Creative Discovery and Symbiogenesis, for which we provide rigorous, quantitative success criteria. This paper offers a complete, falsifiable, and philosophically coherent blueprint for an intelligence that is, by its very nature, radically free and inherently robust.

Esta pesquisa busca contribuir com o desenvolvimento de um prototipo em Python que sera agregado a Plataforma Web Hybroo em desenvolvimento em dissertacao de mestrado no laboratorio LEMM da PUC Goias, com codigo aberto por meio do repositorio do GitHub(https://github.com/edgarancioto/Hybroo). Na Hybroo ja estao disponiveis algoritmos de otimizacao (simples e hibridos) para o problema do roteamento de veiculos. Com este prototipo espera-se a agregacao da otimizacao de funcoes multimodais e mul- tidimensionais por meio de algoritmos deterministicos de ordem 1 (metodo da maxima descida, gradiente conjugado), metodos de ordem zero (Bissecao, secao aurea, Fibonacci) e meta-heuristicas enxame de particulas (PSO), colonia de vagalumes (ACV) e algoritmo genetico (AG). Ate o final da pesquisa serao disponibilizados ainda os metodos hibridos tambem para as funcoes, a partir dos metodos simples ja implementados. Na fase atual foi construido um banco com 40 de funcoes de benchmark [1]. Os metod...

Drones or unmanned aircraft are commonly known as unmanned aerial vehicles (UAVs), and the ad hoc network formed by these UAVs is commonly known as Flying Ad Hoc Network (FANET). UAVs and FANET were initially associated with military surveillance and intelligence gathering; moreover, they are now excessively used in civilian roles including search and rescue, traffic monitoring, firefighting, videography, and smart agriculture. However, due to the distinctive architecture, they pose considerable design and deployment challenges, prominently related to routing protocols, as the traditional routing protocols cannot be used directly in FANET. For instance, due to high mobility and sparse topology, frequent link breakage and route maintenance incur high overhead and latency. In this paper, we employ the bio-inspired Ant Colony Optimization (ACO) algorithm called “Ant-Hocnet” based on optimized fuzzy logic to improve routing in FANET. Fuzzy logic is used to analyze the information about ...

This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.

This paper presents the development and performance evaluation of an Enhanced Chicken Swarm Optimization (ECSO) algorithm for hyperparameter tuning in Convolutional Neural Networks (CNNs), tailored specifically for handwritten document recognition. The ECSO algorithm integrates Gaussian and Tent chaotic maps into the standard Chicken Swarm Optimization framework to enhance the exploration of the solution space and overcome premature convergence. The model was applied to classify original, disguised, and forged handwritten samples using a deep learning framework implemented in MATLAB R2020a. Performance metrics such as accuracy, precision, false positive rate, and execution time were used to assess model effectiveness. Experimental results of the ECSO-CNN model demonstrated strong recognition accuracy (92.14%-95.00%), high precision (95.40%-97.70%), low false positive rates (4%-8%), and fast execution time (22-28 seconds). These outcomes affirm the robustness and efficiency of the ECSO algorithm in optimizing deep learning architectures for forensic purposes. The study highlights the practical utility of chaosenhanced metaheuristic algorithms in legal, security, and archival document authentication systems, positioning ECSO-CNN as a viable solution for scalable and intelligent forensic handwriting analysis.