Nature-Inspired Computing

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.

Crow search algorithm is one of bio-inspired optimization algorithms which is essentially derived for solving continuous based optimization problems. Although many main-frame discrete optimizers are available, they still have some performance challenges. This paper proposes three discrete crow inspired algorithms for enhancing the performance of the original crow search algorithm when it is applied for solving discrete traveling salesman problems. The proposed algorithms are derived based on modular arithmetic, basic operators and dissimilar solutions techniques. Each technique guarantees switching from continuous spaces into discrete spaces without losing information. Such algorithms are called Modular Arithmetic, Basic Operators, and Dissimilar Solutions algorithms. For evaluating their performance, the proposed algorithms are compared with the most state-of-the-art discrete optimizers for solving 111 instances of traveling salesman problems. Simulation results illustrate that, the performance of the proposed algorithms is much better than the performance of most state-of-the-art discrete optimizers in terms of the average optimal solutions accuracy, the average errors from the optimal solutions and the average of computational time.

The traveling transportation problem (TTP) is one of the classic algorithmic problems like the traveling salesman problem (TSP) in the field of computer science, operations research and logistics engineering. It has been classified as the NP-complete problems which can be effectively solved by metaheuristic searching techniques based on modern optimization context. Recently, the flower pollination algorithm (FPA) was developed and proposed as one of the most efficient population-based metaheuristic optimization searching techniques to solve continuous and combinatorial optimization problems. Moreover, the FPA's algorithm is not complex. In this paper, the FPA is applied to solve the large scale traveling transportation problems (LsTTP) consisting of more than 500 destinations. The FPA is tested against six standard LsTTP problems from literatures. Results obtained by the FPA will be compared with those obtained by the genetic algorithm (GA) and the particle swarm optimization (PSO). As results, the FPA can provide optimal solutions for all selected LsTTP problems superior to GA and PSO within shorter computational time.

VHAAI means Very Highly Advanced Artificial Intelligence. ISVHAAI means International Society for VHAAI. VHAAI field is used by ISVHAAI Society in an attempt to solve problems. A new algorithm titled Friendship Human Swarm Optimization (FdHSO) is designed in this Letter No. 8 of ISVHAAI AI Society Letters.

VHAAI and ISVHAAI stands for Very Highly Advanced Artificial Intelligence and International Society for Very Highly Advanced Artificial Intelligence respectively. This is the ISVHAAI Artificial Intelligence Society Letter No. 7 in which a novel algorithm titled Family Human Swarm Optimization (FHSO) is designed.

The relation between Very Highly Advanced Artificial Intelligence (VHAAI) and International Society for Very Highly Advanced Artificial Intelligence (ISVHAAI) is that ISVHAAI AI Society uses VHAAI field to address various problems. In this Letter No. 6 of ISVHAAI Letters, a new algorithm titled "Scientist Swarm Optimization (SSO)" is designed.

is more than a language. It produces real time behaviors of physical systems: computation is the way nature is. Cellular automata as explored by Wolfram are a whole fascinating computational universe. Do they exhaust all possible computational behaviors that our physical universe exhibit? If we understand physical processes as computations in a more general sense than the computations performed by symbol manipulation done by our current computers, then universal Turing machines and universal cellular automata exhibit only a subset of all possible information-processing behaviors found in nature. Even though mathematically, there is a principle of computational equivalence, in physical nature exists a hierarchy of emergent processes on many levels of organization that exhibits different physical behavior and thus can be said compute with different expressive power. This article argues that, based on the notion of computing nature, where computing stands for all kinds of information processing, the development of natural computationalism have a potential to enrich computational studies in the same way as the explorations in the computational universe hold a promise to provide computational models applicable to the physical universe. Cosmogonies as accounts of the origin and the nature of the universe evolve with growth of human knowledge through allegories, myths, models, theories and paradigms. This development goes in parallel with the increase in the size of the known universe -from

The recent development of the research field of Computing and Philosophy has triggered investigations into the theoretical foundations of computing and information. This thesis is the outcome of studies in two areas of Philosophy of Computing (PC) and Philosophy of Information (PI) -the production of meaning (semantics) and the value system with applications (ethics). The first part develops a unified dual-aspect theory of information and computation, in which information is characterized as structure, and computation is the information dynamics. This enables naturalization of epistemology, based on interactive information representation and communication. In the study of systems modeling, meaning, truth and agency are discussed within the framework of the PI/PC unification. The second part of the thesis addresses the necessity of ethical judgment in rational agency illustrated by the problem of information privacy and surveillance in the networked society. The value grounds and sociotechnological solutions for securing trustworthiness of computing are analyzed. Privacy issues show the need for computing professionals to contribute to understanding of the technological mechanisms of Information and Communication Technology. The main original contribution of this thesis is the unified dual-aspect theory of computation/information. Semantics of information is seen as a part of the data-information-knowledge structuring, in which complex structures are self-organized by the computational processing of information. Within the unified model, complexity is a result of computational processes on informational structures.

Starting with the Søren Brier's Cybersemiotic critique of the existing practice of Wissenshaft, this article develops the argument for an alternative naturalization of knowledge production. It presents the framework of natural info-computationalism, ICON, as a new Natural Philosophy based on concepts of information (structure) and computation (process). In this approach, which is a synthesis of informationalism (the view that nature is informational) and computationalism (the view that nature computes its own time development), computation is in general not a substrate-independent disembodied symbol manipulation. Based on the informational character of nature, where matter and informational structure are equivalent, information processing in general is embodied and in general substrate specific. The Turing Machine model of abstract discrete sequential symbol manipulation is a subset of the Natural computing model. With this generalized idea of Natural computing and Informational Structural Realism, Info-computationalism (ICON), adopting scientific third-person account, covers the entire list of requirements for naturalist knowledge production framework from (Brier 2010) except for qualia as experienced in a first-person mode. In his article Cybersemiotics: An Evolutionary World View Going Beyond Entropy and Information into the Question of Meaning (Brier 2010), Søren Brier rightly criticizes the present state of scientific understanding of nature, ("life, consciousness and cultural meaning all as a part of nature and evolution, including humans and other living beings"), specifically: 1. The physico-chemical scientific paradigm based on third person objective empirical knowledge and mathematical theory, but with no conceptions of experiental life, meaning

The bird mating optimizer is a new metaheuristic algorithm that was originally proposed to solve continuous optimization problems with a very promising performance. However, the algorithm has not yet been applied for solving combinatorial optimization problems. Thus, the formulation may not be able to generate a discrete feasible solution. Many continuous algorithms used random-key representation to represent the discrete solution using real numbers or a discrete variant of the algorithm is used to deal with the discrete solution of the problem. However, there is no evidence which methodology is better for solving combinatorial optimization problems. Therefore, this work proposes two variants of bird mating optimizer (random-key bird mating optimizer and the discrete bird mating optimizer), to identify which one is more efficient in solving combinatorial optimization problems. In the first one, we use a random-key encoding scheme, whilst, in the later one, we use crossover (multi-parent) and mutation operators to combine the components of the selected parents to generate new broods. The performance of these algorithms is tested on the travelling salesman problem and berth allocation problem, and are compared with the results of two well-known optimization algorithms: Genetic Algorithm and Particle Swarm Optimization. Experimental results show that the discrete bird mating optimizer is more efficient than the others on all tested benchmark instances. Indeed, it is able to attain the best-known results in some of the BAP benchmark instances. These indicate the applicability and the effectiveness of the proposed discrete bird mating optimizer in solving combinatorial optimization problems. INDEX TERMS Heuristics, bird mating optimizer, berth allocation problem, travelling salesman problem, random-key, combinatorial optimization, metaheuristics.

Optimizing water distribution systems is an essential part of water resources allocation planning. It leads to challenging combinatorial optimization problems, for which meta-heuristics have been applied, notably genetic algorithms and ant colony optimization. The present paper introduces the application of the physarum algorithm, a recent biologically inspired algorithm, utilized hitherto for path finding problems in networks. Direct comparison is presented to ant colony optimization applied to a typical water supply system.

Optimization issues are an inherent part of various disciplines like engineering, economics, and artificial intelligence to solve problems of resource allocation and training neural networks. Classical methods of optimization such as gradient descent and Newton's method give successful answers, although they do not work with challenges such as non-convexity, local optimum, and high dimension. In this paper, an attempt to present new numerical methods of optimizing classical optimization methods is made, applying the Rosenbrock function as a performance metric. The focus of the research is on the metaheuristic algorithms, for example, genetic algorithms (GAs) and particle swarm optimization (PSO), the machine learning-based optimized algorithms, and the mixed algorithms that combine conventional and metaheuristic algorithms. These alternatives are notable for their benefits in solving non-convex, multimodal, or noisy tasks by avoiding getting stuck in local minima and expanding the search in many areas of the solution's space. The study also incorporates algorithm implementation in the Python programming language and the use of Python coding to depict results in detail, giving a real outlook on the different optimization techniques to be used. Thus, the presented results show that other numerical techniques can enhance the optimization results for such issues-setting the base for further developments in the given field.

There are many different nature-inspired algorithms in the literature, and almost all such algorithms have algorithm-dependent parameters that need to be tuned. The proper setting and parameter tuning should be carried out to maximize the performance of the algorithm under consideration. This work is the extension of the recent work on parameter tuning by Joy et al. (2024) presented at the International Conference on Computational Science (ICCS 2024), and the Firefly Algorithm (FA) is tuned using three different methods: the Monte Carlo method, the Quasi-Monte Carlo method and the Latin Hypercube Sampling. The FA with the tuned parameters is then used to solve a set of six different optimization problems, and the possible effect of parameter setting on the quality of the optimal solutions is analyzed. Rigorous statistical hypothesis tests have been carried out, including Student's t-tests, F-tests, non-parametric Friedman tests and ANOVA. Results show that the performance of the FA is not influenced by the tuning methods used. In addition, the tuned parameter values are largely independent of the tuning methods used. This indicates that the FA can be flexible and equally effective in solving optimization problems, and any of the three tuning methods can be used to tune its parameters effectively.

In this paper, we will examine numerous optimization approaches in the field of computer science engineering in depth, shedding light on their applications, strengths, and weaknesses. Optimization algorithms are important tools in computer science engineering, with applications spanning from machine learning to computer vision, data mining, robotics, and more. In principle, optimization algorithms strive to locate the best possible solution among a group of possibilities while taking certain objectives and restrictions into account. They are the foundation of problem-solving approaches, providing a systematic and efficient approach to dealing with multiple difficulties. The efficiency and efficacy of each algorithm vary from one another, and each algorithm has advantages and limits that rely on the applications they are used with. We intend to provide a comprehensive view of optimization algorithms. We will cover their many types, delving into their real-world applications and painstakingly analyzing their strengths and weaknesses. In addition, we will investigate the complexities of each algorithm, giving light on the specific characteristics and settings in which they shine. This work seeks to serve as a basic resource for computer science engineering academics and practitioners, developing a deeper understanding of optimization algorithms and stimulating more inquiry in this dynamic field.

Particle Swarm Optimization (PSO) is a popular and widely used optimization algorithm for solving complex problems. It is known for its simplicity and ease of implementation. Artificial Birds move in search space to find optimal solution. Although many PSO algorithms were proposed in literature the concepts like happiness and health are not yet explored in PSO algorithms. This article is based on this research gap. Happiness and Health Particle Swarm Optimization (HaHePSO) algorithm is created by incorporating the Happiness and Health concepts into Particle Swarm Optimization algorithm. Each particle in HaHePSO algorithm is associated with happiness and health variables. The movement of Artificial Birds in PSO algorithm is based on fitness values. In HaHePSO algorithm the movement of Artificial Birds is dependent on happiness, health and fitness values. In PSO algorithm Artificial Birds move in the direction of local best and global best of fitness values. This idea is extended in HaHePSO algorithm where Artificial Birds move in the direction of local best and global best of happiness, health and fitness values. The HaHePSO algorithm proposed in this article takes more space and requires extra computation compared to PSO algorithm. This is due to the fact that each particle now has happiness and health variables associated with it and movement in search space is guided by the fitness, happiness and health values.

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.

Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure that the algorithm used for optimization performs well and is sufficiently robust for solving different types of optimization problems. In this study, the Firefly Algorithm (FA) is used to evaluate the influence of its parameter values on its efficiency. Parameter values are randomly initialized using both the standard Monte Carlo method and the Quasi Monte-Carlo method. The values are then used for tuning the FA. Two benchmark functions and a spring design problem are used to test the robustness of the tuned FA. From the preliminary findings, it can be deduced that both the Monte Carlo method and Quasi-Monte Carlo method produce similar results in terms of optimal fitness values. Numerical experiments using the two different methods on both benchmark functions and the spring design problem showed no major variations in the final fitness values, irrespective of the different sample values selected during the simulations. This insensitivity indicates the robustness of the FA.

Emergency response preparedness increases disaster resilience and mitigates its possible impacts, mostly in public health emergencies. Prompt activation of these response plans and rapid optimization of delivery models and are essential for effective management of emergencies and disaster. In this paper, existing computational models and algorithms for routing deliveries and logistics during public health emergencies are identified. An overview of recent developments of optimization models and contributions, with emphasis on their applications in situations of uncertainties and unreliability, as obtainable in low-resource countries, is presented. Specific recent improvements in biologically-inspired and intelligent algorithms, genetic algorithms, and artificial immune systems techniques are surveyed. The research gaps are identified, and suggestions for potential future research concentrations are proffered.

Metaheuristic algorithms have proved to be good solvers for the traveling salesman problem (TSP). All metaheuristics usually encounter problems on which they perform poorly so the programmer must gain experience on which optimizers work well in different classes of problems. However due to the unique functionality of each type of meta-heuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this paper, solution to the traveling salesman problem was implemented using genetic algorithm and simulated annealing. These algorithms were compared based on performance and results using several benchmarks. It was observed that Simulated Annealing runs faster than Genetic Algorithm and runtime of Genetic Algorithm increases exponentially with number of cities. However, in terms of solution quality Genetic Algorithm is better than Simulated Annealing.

This paper presents a model for constrained multiobjective optimization of mixedcropping planning. The decision challenges that are normally faced by farmers include what to plant, when to plant, where to plant and how much to plant in order to yield maximum output. Consequently, the central objective of this work is to concurrently maximize net profit, maximize crop production and minimize planting area. For this purpose, the generalized differential evolution 3 algorithm was explored to implement the mixed-cropping planning model, which was tested with data from the South African grain information service and the South African abstract of agricultural statistics. Simulation experiments were conducted using the non-dominated sorting genetic algorithm II to validate the performance of the generalized differential evolution 3 algorithm. The empirical findings of this study indicated that generalized differential evolution 3 algorithm is a feasible optimization tool for solving optima...

The optimal utilization of dams water resources in order to meet the water needs of different departments is an important problem in the management and engineering of water resources. Evolutionary algorithms have provided great success to create balancing in supply of water resources shortages with the goal of controlling the water resources storage and dams release rates. Differential evolution (DE) is an efficient search technique and method to solve numerical optimization problems and realworld applications. DE suffers from numerous weaknesses that usually due to the use of a mutation strategy and consideration of constant settings for parameters. In this paper, an adaptive DE algorithm titled Combined Improved Multi-Population Ensemble DE (CIMPEDE) is suggested. In CIMPEDE, the entire population is divided into four sub-populations; three mutation strategies are considered to achieve a better, and two adaptive and self-adaptive schemas are proposed for setting parameters. CIMPEDE has been able to achieve the optimal solution with the highest quality by choosing the best mutation strategy in each repetition, dynamic adjustment of parameters and exchange of information between subpopulations. For prove the performance of CIMPEDE, extensive experiments have been performed on mathematical benchmark functions (entitled CEC 2013) and a real-world problem for modeling of the singlereservoir system of Golestan dam in Iran with the aim of decreasing irrigation deficiencies. CIMPEDE has also made comprehensive comparisons with DEbased applied approaches. In the case of Golestan dam problem, mean absolute error between real demand and released water by CIMPEDE was zero MCM. While, the comparable algorithm in second rank could not get a value better than 1.81E-220 MCM. Also, in test of reliability and vulnerability indices and test of mean violations intensity between demand and released water in an annual average, the best results were recorded among the comparable evolutionary algorithms for CIMPEDE.

Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some common drawbacks of these algorithms are their premature convergence and the solution found may not be a global optimum. We propose a general, simple and effective strategy, called heterogeneous Perturbation–Projection (HPP), to enhance an algorithm’s exploration capability so that our sufficient convergence conditions are guaranteed to hold and the algorithm converges almost surely to a global optimum. In summary, HPP applies stochastic perturbation on half of the swarm agents and then project all agents onto the set of feasible solutions. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and Ant Colony Optimization for continuous domains ...

The goal of this work is to suggest a new hybrid algorithm to solve integer programming by incorporating the bat algorithm with direct search methods. The suggested algorithm is named hybrid bat direct search algorithm (HBDS). In HBDS, the global diversification and the local intensification process are balanced. The bat algorithm has a good capability to make intensification and diversification search. The intensification ability of the suggested algorithm is increased by employing the pattern search method as a local search method instead of the random walk method in the classic bat algorithm. In the final stage of the algorithm, the Nelder-Mead method is used to improve the best found solution from the bat and pattern search method instead of running the algorithm more iterations without any enhancements in the fitness function value. The performance of the HBDS algorithm is examined on 7 integer programming problems and compared to 10 benchmark algorithms for solving integer programming problems. The computational results show that HBDS is a promising algorithm and outperforms the other algorithms in most cases.

In this paper, we propose a new hybrid algorithm by combining the particle swarm optimization with a genetic arithmetical crossover operator after applying a modification on it in order to avoid the problem of stagnation and premature convergence of the population. In the final stage of the algorithm, we applied the Nelder-Mead method as a local search method in order to accelerate the convergence and avoid running the algorithm without any improvements in the results. We call the new proposed algorithm by simplex particle swarm optimization with a modified arithmetical crossover (SPSOAC). We test SPSOAC on 7 integer programming optimization benchmark functions, 10 minimax problems and 10 CEC05 functions. We present the general performance of the proposed algorithm by comparing SPSOAC against 13 benchmark algorithms. The Experiments results show the proposed algorithm is a promising algorithm and has a powerful performance.

In this paper, a bi-objective Operating Theater scheduling is proposed. The problem is subject to order and assignment constraints. The first objective is the minimization of the operating theater opening total time also called makespan in manufacturing systems while the second is to maximize constraints satisfaction. The scheduling problem is considered as a two-stage hybrid flow shop with blocking. Several metaheuristics are compared: the firefly algorithm, bats algorithm, particles swarm optimization and local search. In addition to the care specific qualitative and quantitative parameters, the average deviation from the lower bound is used in order to confirm the effectiveness of the methods. The implementation is done on the operating theater of the paediatric hospital of Oran when it is properly and improperly sized.

The paper presents an approach to generate and optimize test sequences from the input UML activity diagram. For this, an algorithm is proposed called Unified Modelling Language for Test Sequence Generation (UMLTSG) that uses a search-based algorithm, named Test Sequence Prioritization using Ant Colony Optimization (TSP ACO) to generate and optimize test sequences. The algorithms overcome the existing limitations of handling complex decision-making activity such as conditional activity, fork activity, and join the activity. The optimization process helps to reduce the number of processing nodes that leads to minimizing the time and cost. The proposed approach experiments on a well-known application Railway Ticket Reservation System (RTRS). APFD metric measures the effectiveness of our approach and found that the prioritized order of test sequences achieved 20% higher APFD score. Apart from this, the authors have also experimented on six real life case studies and obtained an average of 52.16% reduction in redundant test paths.

These days the number of issues that we can not do on time is increasing. In the mean time, scientists are trying to make questions simpler and using computers. Still, more problems that are complicated need more complex calculations by using highly advanced technology. Grid computing integrates distributed resources to solve complex scientific, industrial, and commercial problems. In order to achieve this goal, an efficient scheduling system as a vital part of the grid is required. In this paper, we introduce CUckoo-Genetic Algorithm (CUGA), which inspired from cuckoo optimization algorithm (COA) with genetic algorithm (GA) for job scheduling in grids. CUGA can be applied to minimize the completion time of machines, and it could avoid trapping in a local minimum effectively. The results illustrate that the proposed algorithm, in comparison with GA, COA, and Particle Swarm Optimization (PSO) is more efficient and provides higher performance.

Structural designs are progressively more conditioned by uncertainty in a wide range of fields, and new designs have to meet the requirements of safety and efficiency. Probabilistic optimization is a powerful tool able to improve and optimize initial designs into others which are more efficient. In spite of the continuous growth of computational power, this kind of optimization usually turns out to be unaffordable, due to the computational cost of the methods involved. However, parallel computing and surrogate models can overcome this drawback by reducing drastically the total computational cost of the process. In this paper, a survey of the performance of several surrogate models combined with reliabilitybased design optimization is presented. Two examples are selected to show the behavior of the methods when applied to different models.

Exploring free space (scouting) efficiently is a non-trivial task for organisms of limited perception, such as the amoeboid Physarum polycephalum. However, the strategy behind its exploratory behaviour has not yet been characterised. In this organism, as the extension of the frontal part into free space is directly supported by the transport of body mass from behind, the formation of transport channels (routing) plays the main role in that strategy. Here, we study the organism's exploration by letting it expand through a corridor of constant width. When turning at a corner of the corridor, the organism constructed a main transport vein tracing a centre-in-centre line. We argue that this is efficient for mass transport due to its short length, and check this intuition with a new algorithm that can predict the main vein's position from the frontal tip's progression. We then present a numerical model that incorporates reaction-diffusion dynamics for the behaviour of the organism's growth front and current reinforcement dynamics for the formation of the vein network in its wake, as well as interactions between the two. The accuracy of the model is tested against the behaviour of the real organism and the importance of the interaction between growth tip dynamics and vein network development is analysed by studying variants of the model. We conclude by offering a biological interpretation of the well-known current reinforcement rule in the context of the natural exploratory behaviour of Physarum polycephalum.

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.

Vehicle navigation technologies have been around for some time. Even big companies like Google have developed their map service. However, we are trying to develop a navigation system that is better than the ones available in the sense that it provides the user with the most optimum route to his destination, rather than the most shortest route. In this paper, we explore and review various existing technologies, techniques and work being done on the same. This paper, focuses on optimum route guidance considering both static as well as dynamic parameters like traffic density, and traffic clearance rate. To realize optimum route guidance system, Real Time Traffic Congestion Analysis i.e. RTTCA is proposed using A* algorithm based on real time traffic information.

This study proposes several alternative optimal routes on traffic-prone routes using Ant Colony Optimization (ACO) and Firefly Algorithm (FA). Two methods are classified as the metaheuristic method, which means that they can solve problems with complex optimization and will get the solution with the best results. Comparison of alternative routes generated by the two algorithms is measured based on several parameters, namely alpha and beta in determination of the best alternative route. The results obtained are that the alternative route produced by FA is superior to ACO, with an accuracy of 88%. This is also supported by the performance of the FA algorithm which is generally superior, where the resulting alternative route is shorter in distance, time, running time and there is no influence on the alpha parameter value. But in each iteration, the number of alternative routes generated is less. The contribution of this research is to provide information about the best algorithm between ACO and FA in providing the most optimal alternative route based on the fastest travel time. The recommended alternative path is a path that is sufficient for cars to pass, because the selection takes into account the size of the road capacity.

An improved Firefly Algorithm named Repulsion Propulsion Firefly Algorithm (PropFA) is proposed.Parameters odf Firefly Algorithm were made adaptive in nature. • CEC2013 28 benchmarks test suits are employed to test and compare PropFA with several other algorithms. • PropFA was applied for the the purpose Spill Area Estimation of a fast expanding oil spill .The swarm agents were directly translated to a a physical robot(Unmanned Aerial Vehicle). • Experimental results of the PropFA based confinement strategy was noted to be successfull in confining,tracking and area estimation of a spill area upto 16k.m^2 with a total swarm drone population of 30.

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.

In this paper, Firefly algorithms (FA) and Genetic algorithms (GA) are applied to parameter identification problem of a non-linear mathematical model of the E. coli cultivation process. A system of ordinary differential equations is proposed to model the growth of the bacteria, substrate utilization and acetate formation. Parameter optimization is performed using a real experimental data set from an E. coli MC4110 fed-batch cultivation process. In the considered non-linear mathematical model, the parameters that should be estimated are maximum specific growth rate, two saturation constants and two yield coefficients. Parameters of both meta-heuristics are tuned on the basis of several pre-tests according to the optimization problem considered here. Based on the numerical and simulation result, it is shown that the model obtained by the FA is more accurate and adequate than the one obtained using the GA. Presented results prove FA superiority and powerfulness in solving non-linear dynamic model of cultivation processes. 164 Roeva O. and Trenkova T..

In this paper, Firefly algorithms (FA) and Genetic algorithms (GA) are applied to parameter identification problem of a non-linear mathematical model of the E. coli cultivation process. A system of ordinary differential equations is proposed to model the growth of the bacteria, substrate utilization and acetate formation. Parameter optimization is performed using a real experimental data set from an E. coli MC4110 fed-batch cultivation process. In the considered non-linear mathematical model, the parameters that should be estimated are maximum specific growth rate, two saturation constants and two yield coefficients. Parameters of both meta-heuristics are tuned on the basis of several pre-tests according to the optimization problem considered here. Based on the numerical and simulation result, it is shown that the model obtained by the FA is more accurate and adequate than the one obtained using the GA. Presented results prove FA superiority and powerfulness in solving non-linear dynamic model of cultivation processes. 164 Roeva O. and Trenkova T..

Advances in computer technology, coupled with the intention to utilize the limited resources to its best possible way while conforming to the prescribed objective, has led to a wealth of different optimization approaches in engineering problems. Of particular note is the rise of using evolutionary algorithms in obtaining the optimal solutions in the engineering design problems. This paper sets out to compare the optimization performances of three recently developed evolutionary algorithms, namely, bat algorithm, cuckoo search algorithm, and flower pollination algorithm in constrained engineering optimization. Three infamous constrained real world problems, specifically, spring design, welded beam design, and pressure vessel design, are considered in this study. The promising optimization capabilities of all reviewed evolutionary algorithms are shown in the performance assessment.

h i g h l i g h t s • Surveys algorithms applicable to swarm robotic systems for target search and tracking. • Identifies variations of the search and tracking problem addressed in the literature. • Discusses desired capabilities of search and tracking algorithms for robot swarms.

Given the interest and complexity, of the hybrid flow shops (HFS) problem in industry, he has been extensively considered, the HFS, is a complex combinatorial problem supported in many original world applications. We consider a hybrid flow shop FH4(P3, P2)||Cmax to applied in this paper. In this papers we attempt to optimize the makespan which refers to the last task completion time by an adequate meta-heuristic algorithm based on electromagnetism mechanism (EM). We also present analysis on the performance of the EM-algorithm adapted to HFS scheduling problems. The electromagnetism-like mechanism method gave us efficient and fair results comparing to particle swarm and genetic algorithm.

Facility layout is the arrangement of machines, equipments or other resources in a manufacturing environment to designate an ideal configuration for minimizing the total cost by affecting the production flow. Layout design has a significant impact on the performance of manufacturing systems, and the layout problems are generally regarded as NP-Hard problems. In literature, a considerable amount of attention is granted to biology-inspired metaheuristic algorithms in order to find efficient solutions to deal with many optimization problems. In this study, the general features and the mechanism of the Firefly Algorithm are presented initially. In order to illustrate how to adapt the proposed algorithm to a real manufacturing problem, a numerical application is shown for the solution of single row facility layout problem. A candidate solution array for 15 departments is obtained through the presumptions of the proposed algorithm. For a sample size of 500 iterations, %95 confidence interval is constructed between the values of 8306.53 and 8378.22 with a standard error value of 18.288.

The necessity of transporting goods from production facilities to buyers requires every company to manage logistics. While the quantity of products ordered has been decreasing in recent years, the number of orders has been increasing. This situation leads to higher logistics costs and more attempts to control logistics costs by business managers. One way to decrease logistics costs is the optimization of traveled distances. The Traveling Salesman Problem (TSP) attempts to optimize travel distances by changing the order of the locations to be visited. By doing so, it reduces the logistics costs associated with travel distances. However, there are also some parameters of logistics costs that are not related to travel distances. This paper examines the effects of optimization results by TSP on logistics costs, using seven different methods to consider a real logistics problem, and comparing the results. Then it discusses the variation in logistics costs due to TSP.

Metaheuristic algorithms are found to be promising for difficult and high dimensional problems. Most of these algorithms are inspired by different natural phenomena. Currently, there are hundreds of these metaheuristic algorithms introduced and used. The introduction of new algorithm has been one of the issues researchers focused in the past fifteen years. However, there is a critic that some of the new algorithms are not in fact new in terms of their search behavior. Hence, a comparative study in between existing algorithms to highlight their differences and similarity needs to be studied. Apart from knowing the similarity and difference in search mechanisms of these algorithms it will also help to set criteria on when to use these algorithms. In this paper a comparative study of prey predator algorithm and firefly algorithm will be discussed. The discussion will also be supported by simulation results on selected twenty benchmark problems with different properties. A statistical analysis called Mann-Whitney U 2 test is used to compare the algorithms. The theoretical as well as simulation results support that prey predator algorithm is a more generalized search algorithm, whereas firefly algorithm falls as a special case of prey predator algorithm by fixing some of the parameters of prey predator algorithm to certain values.

Expert Systems are entering a critical stage as interest spreads from university research to practical applications. The proposed Expert system is expected to solve the problems faced in an educational institute for gathering information for management level from the faculty level. Depending on the level of authorization the system will respond and provide a satisfactory answer for the query posed. Both essential and desirable features of an expert system are discussed. Components of an adaptive system are identified, with an emphasis on the mechanisms that enable adaptive behavior to occur. Knowledge representation in a rule-based, object-orientated expert system is described through the establishment of appropriate relationships utilizing heuristic rules, objects, and agents. The experimental expert system displays low level learning capabilities that show sufficient promise to warrant further research.

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important transportation, logistics, and manufacturing systems applications. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO by incorporating crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library, and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 9...

Several Human-Inspired Metaheuristic Optimization Algorithms were proposed in literature. But the concept of Devotees-Inspired Metaheuristic Optimization Algorithms is not yet explored. In this article, Lord Rama Devotees Algorithm (LRDA) is proposed which is a new Devotees-Inspired Metaheuristic Optimization Algorithm.