Firefly Algorithm

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.

Principally, it is important to provide the customer with reliable and adequate power. As, power quality is measured by the stability in frequency and power exchange among control areas. Accordingly, automatic generation control (AGC) plays a vital role in power system operation and control. In this article, a renewable energy source (RES) such as wind turbine system (WTS) is added into the thermal unit in each area. The inclusion of wind turbine system has better frequency and power line exchange profile as demonstrated in the result analysis.  An attempt is made to apply the successful evolutionary optimization technique named as firefly algorithm for multi objective, such as controller gains, set point weights etc. The firefly algorithm reduces the required control effort for achieving the dynamic response of system like settling time, overshoot and integral square error.  Furthermore the stability aspects of the system are taken care of by applying different criteria and analysi...

Price prediction has now become an important task in the operation of electrical power system. In short term forecast, electricity price can be predicted for an hour-ahead or day-ahead. An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour. It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most of the power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than singlesettlement system (real time). Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by three stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.

The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.

Power flow and state estimation are fundamental processes in the operation and planning of power systems. This paper presents a brief review, implementation, and evaluation of these techniques on a benchmark IEEE three-generator, six-bus network. The Newton-Raphson method is employed to solve the nonlinear AC power flow equations, with results validated against industrial software (PSSE). Additionally, weighted least squares algorithms were implemented for both DC and AC models. While the DC estimator achieved an acceptable residual of 5.57 MW in active power estimation, it still exhibited measurable accuracy loss on high-R/X or voltage-deviation branches, with line-flow mean absolute percentage errors increasing by ∼1-3% and bus angle deviations reaching up to 19 • relative to AC estimator results. Ultimately, an overview of how classical estimation and analysis techniques can be practically applied and evaluated for both industrial and benchmarking purposes in power systems engineering are presented and discussed in detail.

http://jpst.ripi.ir Journal of Petroleum Science and Technology 2019, 9(3), 10-26 © 2019 Research Institute of Petroleum Industry (RIPI) ABSTRACT Fluid catalytic cracking (FCC) process is a vital refinery process which majorly produces gasoline. In this research, a hybrid algorithm which was constituted of Adaptive Neuro-Fuzzy Inference System (ANFIS) and firefly optimization algorithm was developed to model the process and optimize the operating conditions. To conduct the research, industrial data of Abadan refinery FCC process were carefully gathered along a definite period. Then a model based on ANFIS was developed to investigate the effect of operating variables including reactor temperature, feed flow rate, temperature of top of main column, and the temperature of bottom of the debutanizer tower on quality and quantity of gasoline, LPG flow rate, and process conversion. Moreover, statistical parameters comprising R2, RMSE, and MRE approved the accuracy of the model. Eventually,...

This paper presents a methodology for optimizing dynamic threshold MOSFET (DTMOS) two-stage operational transconductance amplifiers (OTAs) tailored for biomedical applications through the utilization of the Firefly algorithm. The optimization process focuses on enhancing key performance metrics such as gain, bandwidth, and power efficiency, which are critical for biomedical signal processing, neural interfaces, and wearable healthcare devices. The methodology encompasses circuit architecture definition, Firefly algorithm implementation, fitness evaluation, and result analysis. The optimization results reveal a significant enhancement in performance metrics. Specifically, the number of transistors in the design is 25. The initial overall gain was 76.65 V/V, with a power efficiency (µ) of 1.6. After optimization, the overall gain was significantly improved to 84.029 dB using the Firefly algorithm, demonstrating superior performance compared to existing algorithms. The power efficiency (µ) was also enhanced to 1.702, underscoring the efficiency improvements achieved through optimization. Simulation results and statistical analysis confirm that the Firefly algorithm effectively achieves optimal configurations, improving the robustness of OTA designs against parameter variations. These enhancements validate the algorithm’s efficacy in addressing power-performance trade-offs and its suitability for diverse biomedical applications. Physical prototyping of the optimized design further demonstrates real-world functionality, underscoring its practical applicability.

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.

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 this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.

In this document, the Firefly Algorithm (FA) and Cuckoo Search (CS) algorithm based on optimal location and the capacity of UPFC to improve the dynamic stability of the power system are proposed. The novelty of the proposed method is exemplified in the improved searching ability, random reduction and reduced complexity. In this regard, the generator fault affects the system dynamic stability constraints such as voltage, power loss, real and reactive power. Here, the FA technique optimizes the maximum power loss line as the suitable location of the UPFC. The affected location parameters and dynamic stability constraints are restored into secure limits using the optimum capacity of the UPFC, which in turn, has been optimized with reduced cost by using the CS algorithm. The attained capacity of the UPFC has been located in the affected location and the power flow of the system analyzed. The proposed method is implemented in the MATLAB/Simulink platform and tested under IEEE 30 and IEEE 14 standard bench mark system. The proposed method performance is evaluated by comparison with those of different techniques such as ABC-GSA, GSA-Bat, Bat-FA and CS algorithms. The comparison results invariably prove the effectiveness of the proposed method and confirm its potential to solve the related problems.

The identification of linear dynamic systems with static nonlinearities in the delta domain has been presented in this paper applying a firefly based hybrid meta-heuristic algorithm integrating Firefly algorithm (FA) and Gray wolf optimizer (GWO). FA diversifies the search space globally while GWO intensifies the solutions through its local search abilities. A test system with continuous polynomial nonlinearities has been considered for hammerstein and wiener system identification in continuous, discrete and delta domain. Delta operator modelling unifies system identification of continuous-time systems with discrete domain at higher sampling frequency. Pseudo random binary sequence, contaminated with white noise, has been taken up as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithm not only outperforms the parent heuristics of which they are constituted but also proves better as compared to some standard and lat...

Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combina...

In this study, determination of the optimal proportional-integral-derivate (PID) parameters with Gravitational search algorithm (GSA) for automatic generation control (AGC) of the two area non-reheat thermal power system is proposed. GSA is applied to search for the optimal PID controller parameters to minimize various performance indexes. The designed PID controller with the proposed approach is simulated under variety of operating conditions. Simulation results are shown that dynamic performance of the two area non-reheat thermal power system is improved by the designed PID controller with the proposed approach.

Early identification is essential for successful treatment of melanoma, a potentially fatal type of skin cancer. This work takes a fresh approach to addressing the urgent need for an accurate and economical melanoma categorization system. Inaccuracy, efficiency, and resource usage are common problems with current techniques. A model that incorporates a number of innovative methods to get beyond these restrictions was used in this study. To improve data quality, first applied the preprocessing with a Gaussian filter and augment our dataset with Short-Term Memory (LSTM) networks. The model performs better and is substantially more accurate when Firefly Optimization is used. It analyses the model's ability to lower healthcare costs by doing a cost-effective analysis, especially when detecting melanoma, including situations involving bleeding lesions. The proposed FFO Enhanced Conv-LSTM's cost-effective analysis makes it possible to compare it favourably to deep convolutional neural networks (DCNN), showcasing its promise for melanoma classification accuracy and healthcare resource allocation optimization. For this study, Python software was used as the implementation tool. The suggested model achieves a 99.1% accuracy rate, which is better than current techniques. A comparative study with well-known models such as Res Net 50, Mobile Net, and Dense Net 169 highlights the notable enhancement provided by the proposed Firefly Optimizationenhanced Conv-LSTM method. This model offers a promising advancement in the precise and economical classification of melanoma due to its high accuracy and cost-effectiveness. In comparison to existing approaches like Res Net 50, Mobile Net, and Dense Net 169, the suggested Firefly Optimization-enhanced Convolutional LSTM (FFO Enhanced Conv-LSTM) method shows an average gain of roughly 5.6% in accuracy.

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

Nature is a rich source of inspiration, which has inspired many researchers in many ways. Nowadays, new algorithms have been developed by the inspiration from nature. The flower pollination algorithm is based on the characteristics of pollination process of flowers plants. Pollination is a natural biological process of mating in plants. In flowers, pollen is carried to stigma through some mechanisms that confirm a proper balance in the genetic creations of the species. Another nature inspired algorithmthe Bat algorithm is based on the echolocation behavior of bats. In this paper, the Flower pollination algorithm is compared with the basic Bat algorithm. We have tested these two algorithms on both unimodal and multimodal, low and high dimensional continuous functions. Simulation results suggest that the Flower pollination algorithm can perform much better than the Bat algorithm on the continuous optimization problems.

ABSTRAK Penelitian ini membahas tentang rekonfigurasi jaringan untuk meminimalkan rugi jaringan pada sistem distribusi. Nilai rekonfigurasi jaringan dibatasi oleh jumlah tie switch yang ada pada jaringan distribusi. Rekonfigurasi jaringan Distribusi merupakan salah satu cara yang dilakukan untuk memperbaiki kualitas daya, kualitas daya dalam hal ini adalah perbaikan jatuh tegangan dan rugi – rugi daya. Binary firefly algorithm digunakan untuk optimasi rekonfigurasi jaringan Distribusi IEEE 33 bus. Skema ini diujikan pada sistem 12,66 kV. Hasil simulasi dengan menggunakan binary firefly algorithm pada saat belum dilakuakn optimasi pada sistem distribusi IEEE 33 bus memiliki rugi jaringan sebesar 202,69 kW dengan tegangan minimum 0,91 pu. Setelah rekonfigurasi jaringan rugi jaringan yang optimal sebesar 139,53 kW dengan tegangan minimum 0,95 pu.   Kata kunci: Rekonfigurasi, Rugi-rugi Daya, Fire Fly Algorithm

The Cellular Neural Network is a 2D array of analog processors which forms a parallel computing framework. The main key factors in this model are the values of the neighborhood of each cell, which are called templates. These templates are usually set by a domain expert in this framework to determine the optimal values of the templates. In this paper, a novel approach was proposed to discover the templates of the cellular neural networks based on mutual information and firefly optimization. The mutual information discovers the hidden pattern in the templates by measuring the similarities among cells. The firefly algorithm navigates the search space to find the optimal values of the templates. The benchmarking and validation have been performed on the ChestX-ray8, which is a real-world X-ray images dataset. The proposed method achieved significant results when compared to other meta-heuristics algorithms such as Genetic Algorithm and Particle Swarm Optimization.

In this article fractional order PID (FOPID) controller is optimally designed using Ant Lion Optimizer (ALO) and implemented to study load frequency control (LFC) issue in a two area thermal power system. Study of the transient analysis begins with realization of the two area thermal system from previously published articles based on conventional PI controllers. Then effort is made to improve the performance of the system employing ALO tuned conventional PID and FOPID controllers. Step load change of 1% in area1 of the power system is considered for transient analysis and integral time absolute error (ITAE) is selected as objective function in optimally designing PID and FOPID controllers. It is found that ALO tuned FOPID controlled AGC system performs better as compared to ALO tuned PID, hBFOA-PS, BFOA and GA based PI controller. Robustness analysis of the proposed ALO designed FOPID controller reveals that there is no need to retune the optimized controller parameters in case of s...

This article deals with Automatic Generation Control (AGC) of a three-area power system having five diversified sources of generation like thermal unit, hydro unit, wind unit, diesel unit and a gas unit are interconnected together. Area-1 of the power system consists of a thermal, a hydro and a wind unit, area-2 has a thermal, a hydro and a diesel unit and area-3 consists of a thermal, a hydro and a gas unit. To make system more realistic different nonlinear components like governor dead band (GDB), generation rate constraint (GRC), Boiler dynamics and communication delay are taken into account. A novel two degree of freedom fractional order PID with derivative filter and fractional order PD with derivative filter (2-DOF-FOPIDN-FOPDN) cascaded control strategy is adopted to improve the dynamic performance of the power system. Results obtained with the proposed cascaded controller are compared with that of PID, FOPID and 2-DOF-PIDN-PDN cascaded controller to prove its superiority. To enumerate the gains of different controllers optimally, a recently developed bio-inspired optimisation algorithm named Selfish Herd Optimisation (SHO) is capitalised. Further, the work is extended by taking a two area hydro thermal system to compare the result of the SHO tuned PID controller with that of modern hybrid firefly algorithm-pattern search (hFA-PS) technique. Transient analysis is carried out by applying a sudden load disturbance of 0.01 p.u in area-1 and the robustness of the controller is examined by varying both system parameters and applying a randomly varying load in area-1. From the investigation it is concluded that the 2-DOF-FOPIDN-FOPDN controller gives a flawless and a distinct performance.

Cancer has been a significant threat to human health and well-being, posing the biggest obstacle in the history of human sickness. The high death rate in cancer patients is primarily due to the complexity of the disease and the wide range of clinical outcomes. Increasing the accuracy of the prediction is equally crucial as predicting the survival rate of cancer patients, which has become a key issue of cancer research. Many models have been suggested at the moment. However, most of them simply use single genetic data or clinical data to construct prediction models for cancer survival. There is a lot of emphasis in present survival studies on determining whether or not a patient will survive five years. The personal issue of how long a lung cancer patient will survive remains unanswered. The proposed technique Naive Bayes and SSA is estimating the overall survival time with lung cancer. Two machine learning challenges are derived from a single customized query. To begin with, determi...

This paper describes modeling and control strategies of Permanent Magnet Synchronous Generators (PMSG) connected to lithium-ion battery pack to compensate intermittent energy from wind farm. Two PMSG and battery are connected to electrical grid through a back-to-back converter, and buck-boost converter in battery's side. Full system is connected to AC grid with phase to phase RMS voltage of 20kV. Proposed control strategies are focused on Maximum Power Point Tracking (MPPT) for PMSG speed control, active / reactive power, DC-bus voltage management and battery's power control. To show the control strategies performances, some simulation are done using Matlab / Simulink software.

The perception of automatic generation control (AGC) has a massive part in delivering eminence power in an interrelated structure. To acquire eminence power by monitoring the fluctuations of frequency and tie-link power, an appropriate controller strategy is essential. This work explores AGC learning under the traditional situation. In this study, we employ a cascade controller with proportional amalgamation with a tilt-integral-derivative with a filter (TIDN) and fractional order integral-derivative (FOID), named TIDN-FOID. In order to acquire the controller’s attributes, a meta-heuristic optimization algorithm spotted hyena optimizer (SHO) is employed. Several investigations express the excellency of the TIDN-FOID controller over other controllers from outlook regarding the lessened level of peak_overshoot, peak_undershoot, and settling_time for the considered structure. The structure comprises thermal, biodiesel units in area 1, thermal, and geothermal units in area-2, and hydrot...

The concept of automatic generation control has an immense role in providing quality power in an interconnected system. To obtain quality power by controlling the oscillations of frequency and tie-line power, a proper controller design is necessary. So, an innovative endeavor has been undertaken to enforce a two-stage controller with the amalgamation of a proportional-derivative with filter (PDN) (integer-order) and a fractional order integral-derivative (FOID), i.e., PDN(FOID). In an effort to acquire the controller’s gains and parameters, a bio-inspired meta-heuristic spotted hyena optimizer is applied. Various examinations manifest the excellence of PDN(FOID) over other controllers such as integral, proportional–integral, proportional–integral-derivative filter, and fractional order PID from perspectives concerning the diminished amount of peak anomaly oscillations, and the instant of settling for a three-area system. The system includes thermal–bio-diesel in area-1; a thermal–ge...

This paper aims to apply a modified current search method, adaptive current search (ACS), for assembly line balancing problems. The ACS algorithm possesses the memo ry list (M L) to escape fro m local entrapment and the adaptive radius (AR) mechanis m to speed up the search process. The ACS is tested against five benchmark unconstrained and three constrained optimization problems co mpared with genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms and provides superior results. The ACS is used to address the number of tasks assigned for each workstation, while the heuristic sequencing (HS) technique is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The workload variance and the idle t ime are performed as the mult iple-object ive functions. The proposed approach is tested against four benchmark A LB problems co mpared with the GA, TS and CS. As results, the ACS associated with the HS technique is capable of producing solutions superior to other techniques. In addition, the ACS is an alternative potential algorithm to solve other optimization problems.

FunctionalLinkNeuralNetwork(FLNN)hasemergedasanimportanttoolforsolvingnon-linear classificationproblemandhasbeensuccessfullyappliedinmanyengineeringandscientificproblems. TheFLNNstructureismuchmoremodestthanordinaryfeedforwardnetworkliketheMultilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training.However,thestandardBackpropagation(BP)learningusesforFLNNtrainingpronetoget trapinlocalminimawhichaffecttheFLNNclassificationperformance.TorecovertheBP-learning drawback,thispaperproposesanArtificialBeeColony(ABC)optimizationwithmodificationon beeforagingbehaviour(mABC)asanalternativelearningschemeforFLNN.Thisismotivatedby goodexplorationandexploitationcapabilitiesofsearchingoptimalweightparametersexhibitbyABC algorithm.TheresultoftheclassificationaccuracymadebyFLNNwithmABC(FLNN-mABC) iscomparedwiththeoriginalFLNNarchitecturewithstandardBackpropagation(BP)(FLNN-BP) andstandardABCalgorithm(FLNN-ABC).TheFLNN-mABCalgorithmprovidesbetterlearning schemefortheFLNNnetworkwithaverageoverallimprovementof4.29%ascomparedtoFLNN-BPandFLNN-ABC.

The act of sharing or exchanging data, concepts, or emotions is known as communication. Both parties must be able to speak and comprehend the same language in order for communication to be established. It can be quite challenging for people with speech and hearing problems to communicate with the broader public. The way they communicate is through sign language. To communicate with the outside world, they are dependent on interpreters who use sign language. Being a minority, most people are unaware of the sign language they employ. Sign language communication is always necessary. Without an interpreter, communication might be challenging. One can create a machine learning-based model to get around this obstacle. It is possible to train a model to translate various sign language motions into English by recognizing them. Many individuals will find this useful while interacting and chatting with the deaf and ignorant.

The act of sharing or exchanging data, concepts, or emotions is known as communication. Both parties must be able to speak and comprehend the same language in order for communication to be established. It can be quite challenging for people with speech and hearing problems to communicate with the broader public. The way they communicate is through sign language. To communicate with the outside world, they are dependent on interpreters who use sign language. Being a minority, most people are unaware of the sign language they employ. Sign language communication is always necessary. Without an interpreter, communication might be challenging. One can create a machine learning-based model to get around this obstacle. It is possible to train a model to translate various sign language motions into English by recognizing them. Many individuals will find this useful while interacting and chatting with the deaf and ignorant.

This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. The strength of the proposed meta-heuristic optimization technique CSA has been tested and validated on the standard IEEE 14-bus, 26-bus and 30-bus system with several heuristic load patterns. The results have indicated that the proposed approach is able to obtain significant economic load dispatch solutions than those of Firefly Algorithm (FFA) and other soft computing techniques reported in the literature.

The discovery of Australoluciola thailandica Sriboonlert, Swatdipong, and Sartsanga, new species, in a northern province of Thailand extends the range of the genus previously known only in Australia and New Guinea. A description based on three males is given, and a key to the species of Australoluciola Ballantyne is provided.

Modern power systems must be resilient to sudden load variations in order to keep the system stable. For Automatic Generation Control (AGC), single load change is impractical and need further analysis. This study comprehensively explore the performance of AGC in a two-area interconnected power system, focusing on a wide range load variations that can exists in realistic power systems consisting from 100 to 300 MW in both increments and decrements. The performance of three control strategies-Conventional AGC (CAGC), Tie-Line Bias (TLB) Control, and Genetic Algorithm-Optimized PID (GA-PID)-is assessed across 12 distinct cases, each tested under these three scenarios. A total of 360 tests are conducted, with performance measured by key metrics, including overshoot, undershoot, settling time, and steady-state accuracy for both areas. The results demonstrate that GA-PID consistently outperforms CAGC and TLB in minimizing transient deviations, ensuring faster stabilization, and maintaining steady-state accuracy. For load increases, GA-PID reduces overshoot by up to 90% and eliminates undershoot in several cases. In comparison, CAGC and TLB show notable weaknesses when dealing with larger disturbances, such as extended oscillations and bigger deviations. The results highlight how effective GA-PID is as a strong and flexible control method, which is crucial for today's power systems that need to manage unpredictable changes in load.

In this paper, two metaheuristics are presented for exploration and mine disarming tasks performed by a swarm of robots. The objective is to explore autonomously an unknown area in order to discover the mines, disseminated in the area, and disarm them in cooperative manner since a mine needs multiple robots to disarm. The problem is bi-objective: distributing in different regions the robots in order to explore the area in a minimum amount of time and recruiting the robots in the same location to disarm the mines. While autonomous exploration has been investigated in the past, we specifically focus on the issue of how the swarm can inform its members about the detected mines, and guide robots to the locations. We propose two bioinspired strategies to coordinate the swarm: the first is based on the Ant Colony Optimization (ATS-RR) and the other is based on the Firefly Algorithm (FTS-RR). Our experiments were conducted by simulations evaluating the performance in terms of exploring and disarming time and the number of accesses in the operative grid area applying both strategies in comparison with the Particle Swarm Optimization (PSO). The results show that FTS-RR strategy performs better especially when the complexity of the tasks increases.

In this paper a population based evolutionary optimization methodology called firefly algorithm (FFA) is applied for the optimization of system coefficients of the infinite impulse response (IIR) system identification problem. FFA is inspired by the flash pattern and characteristics of fireflies. In FFA technique, behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and a bright firefly feels more attraction for the brighter one. For this optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic movement equation results in balancing of exploration and exploitation of search space. The proposed FFA based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using FFA over GA, PSO and DE in terms of convergence speed, identifying plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA's loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis.

In recent times, there has been a growing attention to intelligent optimization algorithms centred on swarm principles such as the firefly algorithm (FA). It was proposed for the continuous domain that mimics the attraction of fireflies to flashing light and has been used in discrete domains via modification. A discrete domain that is a major challenge in most higher education institutes (HEI) is examination timetabling. This article presents a new methodology based on FA for uncapacitated examination timetabling problems (UETP) where the proposed method is an extension of earlier work by the authors on the continuous domain. UETP is considered in this article as it is a university examination timetabling problem, which is still an active research area and has not been solved by FA algorithm as per authors knowledge. The proposed method concentrates on solving the initial solution using discrete FA where it consolidates the reordering of examinations and slots through a heuristic ordering known as neighborhood search. Three neighborhoods are employed in this research, where one is used during the initialization phase while two are utilized during solution improvement phase. Later, through preference parameters, a novel stepping ahead mechanism is used, which employs neighborhood searches built on previous searches. The proposed method is tested with 12 UETP problems where the preference based stepping ahead FA creates comparative results to the best ones available in the literature for the Toronto exam timetabling dataset. The results obtained are proof of concept at the preliminary stage and require further experiments on other educational datasets such as the second international timetable competition benchmark sets. The newly introduced preference based stepping ahead mechanism takes advantage of the current best solution space where it exploits the solution space for better solutions. This paves the way for researchers to utilize the mechanism in other domains such as robotics, etc.

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA's loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis.

Robot path planning is a computational problem to find a valid sequence of configurations to move a robot from an initial to a final destination. Several classical and heuristic-based methods exist that can be used to solve the problem. This paper compares the performance of a classical method based on potential field, Lyapunov-based Control Scheme, with those of the standard and stepping ahead Firefly Algorithms. The performance comparison is based on the optimal path distance and time. The results show that the stepping ahead Firefly algorithm finds a shorter path in lesser duration when compared with the Lyapunov-based method. The LbCS also inherently faces the local minima problem when the start, target, and obstacle's center coordinates are collinear. This problem is solved using the firefly algorithm where the diversification of the fireflies helps escape local minima.

Metropolitan regions have experienced higher economical and environmental pressure due to the fasted urbanization leading to increased traffic jams that necessitate the use of higher optimization techniques. Traditional traffic models do not usually take large-dimensional and dynamicity of urban mobility into consideration and require extraordinary computational approaches. Modified Sand Cat Swarm Optimization (MSCSO) improves the Sand Cat Swarm Optimization (SCSO) algorithm that adds Levy flights to global exploration and roulette wheel selection to adaptive exploitation to solve problems that are complex and high-dimensional. When used in urban traffic management, MSCSO works with enormous volumes of traffic, speed, weather, and incident, all of which may decrease Travel Time Index by 15 percent during rush hours. Benchmark tests are used to prove that MSCSO is better, scoring 0.0 in Sphere, Ackley and Rastrigin functions, and 28.0753 in Rosenbrock, whereas higher scores belong to Particle Swarm Optimization, Genetic Algorithms, Ant Colony Optimization and SCSO (e.g., 46). It supports urban planning, since a Flask-based web interface has the possibility to input and visualize real time traffic data in a simple way. The success of MSCSO is reliant on high-quality data and hardware-friendly algorithms but can scale to use real-time data sources, such as from GPS, machine learning traffic projections, and cloud hosting, and is of potential use in logistics, energy delivery, and resource assignment.

Aims: The aim sought is to design a wind energy system can meet the energy needs of a rural household in minimizing both the economic cost and the energy cost of the system over its life cycle while ensuring continuity in the provision of electrical energy. Study Design: Design of a wind system study. Place and Duration of Study: Department of Mechanical Engineering and Energy, Laboratory Energy and Applied Mechanics, between September 2012 and March 2013. Methodology: We have adopted an approach that requires a combination of field work and scientific work. A survey has been conducted in the locality chosen to know the equipment used to determine the consumption profile; some players were involved in determining the weight we assigned to different criteria. The NSGA-II algorithm, evolutionary genetic type was used in the context of determining the set of optimal solutions of compromise. Design variables used are the wind turbines number, batteries number, wind turbine type, battery...

The ease and affordability offered by the cloud computing has attracted large number of customers towards it. Cloud service providers offer its services, to the cloud customers, usually in form of Virtual Machines (VMs). With the growth in the number of customers, cloud data centers encounter overwhelming number of VM requests. These requests need to be mapped on the real cloud hardware and therefore, VM placement has been an important research area in the cloud research community. Virtual machine placement, being an NP hard problem, is modelled as an optimization problem with the objective to optimize resource wastage. Dragonfly Algorithm (DA), a nature inspired technique, originates from static and dynamic swarming behavior of dragonfly and is well suited to solve VM placement problem. Therefore, in the proposed work, a modified dragonfly algorithm is applied for VM placement for better resource utilization at cloud data centers. The performance of the proposed model is analyzed through simulation and comparative study. Observations, obtained from the experiments, exhibit the superiority of the proposed model in solving VM placement problem. Binary dragonfly algorithm (BDA) • Virtual machine (VM) • Cloud data center (CDC) • Cloud service provider (CSP) • NP-hard problem • Resource utilization • Information technology (IT) * Deo Prakash Vidyarthi

Buffer management is very crucial to the Cellular IP networks as its proper use not only increases the throughput of the network but also results in the reduction of call drops. A model for buffer management in Cellular IP network using GA is being proposed in this work. It deals with two kinds of buffers; Gateway buffer and Base Station buffer. This is a two-tier model, where in the first tier a prioritization algorithm is applied for prioritizing real-time packets and to serve them in the buffer of the Gateway within a specified threshold. Remaining packets which couldn't be served after the threshold will be given to the nearest cells of the network to be dealt with in the second part. A GA based procedure is applied here in order to store these packets in the buffer of the base stations. Experiments have been conducted with different numbers of returned packets in order to study the effect of available buffer space on the number of dropped packets and to check the efficiency...