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Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner mohammad dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards.The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after feeding.To assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark functions.These functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test suite.FLO’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative analysis.The simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search process.This balanced approach enables FLO to outperform several competing algorithms in numerous test cases.Additionally,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization challenges.Overall,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

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Migration Algorithm:A New Human-BasedMetaheuristic Approach for Solving Optimization Problems

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1695-1730页

作者： Pavel Trojovsky mohammad dehghani Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic

This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so on.Themathematicalmodeling of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search process.In the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available options.In the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions.MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite.In addition,MA’s results are compared with the performance of twelve well-known metaheuristic algorithms.The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems.The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions.Also,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.

关键词： Optimization metaheuristic migration human-based algorithm exploration exploitation

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Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1527-1573页

作者： Pavel Trojovsky mohammad dehghani Eva Trojovská Eva Milkova Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic

In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education Optimization(LEO)is introduced,which is used to solve optimization problems.LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules.LEO is mathematically modeled in three phases:(i)students selecting their teacher,(ii)students learning from each other,and(iii)individual practice,considering exploration in local search and exploitation in local search.The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal,multimodal types and the CEC 2017 test suite.The optimization results show that LEO,with its acceptable ability in exploration,exploitation,and maintaining a balance between them,has efficient performance in optimization applications and solution presentation.LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms.Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior andmore competitive in combating the compared algorithms.The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.

关键词： Optimization language education exploration exploitation metaheuristic algorithm

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SSABA:Search Step Adjustment Based Algorithm

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Computers, Materials & Continua 2022年第6期71卷 4237-4256页

作者： Fatemeh Ahmadi Zeidabadi Ali dehghani mohammad dehghani Zeinab Montazeri Stepán Hubálovsky Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Civil Engineering Islamic Azad UniversityEstahban BranchEstahbanIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences.Optimization algorithms are one of the effective stochastic methods in solving optimization problems.In this paper,a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm(SSABA)is presented to provide quasi-optimal solutions to various optimization problems.In the initial iterations of the algorithm,the step index is set to the highest value for a comprehensive search of the search space.Then,with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal,the step index is reduced to reach the minimum value at the end of the algorithm implementation.SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types.The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm.In addition,the performance of the proposed SSABA is compared with the performance of eight well-known algorithms,including Particle Swarm Optimization(PSO),Genetic Algorithm(GA),Teaching-Learning Based Optimization(TLBO),Gravitational Search Algorithm(GSA),Grey Wolf Optimization(GWO),Whale Optimization Algorithm(WOA),Marine Predators Algorithm(MPA),and Tunicate Swarm Algorithm(TSA).The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.

关键词： Optimization population-based optimization problem search step optimization algorithm minimization maximization

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MLA:A New Mutated Leader Algorithm for Solving Optimization Problems

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Computers, Materials & Continua 2022年第3期70卷 5631-5649页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari mohammad dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Graduate of Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Optimization plays an effective role in various disciplines of science and engineering.Optimization problems should either be optimized using the appropriate method(i.e.,minimization or maximization).Optimization algorithms are one of the efficient and effective methods in providing quasioptimal solutions for these type of problems.In this study,a new algorithm called the Mutated Leader Algorithm(MLA)is presented.The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader.In addition to information about the best member of the population,themutated leader also contains information about the worst member of the population,as well as other normal members of the population.The proposed MLA is mathematically modeled for implementation on optimization problems.A standard set consisting of twenty-three objective functions of different types of unimodal,fixed-dimensional multimodal,and high-dimensional multimodal is used to evaluate the ability of the proposed algorithm in optimization.Also,the results obtained from theMLA are compared with eight well-known algorithms.The results of optimization of objective functions show that the proposed MLA has a high ability to solve various optimization problems.Also,the analysis and comparison of the performance of the proposed MLA against the eight compared algorithms indicates the superiority of the proposed algorithm and ability to provide more suitable quasi-optimal solutions.

关键词： Optimization metaheuristics leader benchmark objective function

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AMBO:All Members-Based Optimizer for Solving Optimization Problems

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Computers, Materials & Continua 2022年第2期70卷 2905-2921页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari mohammad dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

There are many optimization problems in different branches of science that should be solved using an appropriate methodology.Populationbased optimization algorithms are one of the most efficient approaches to solve this type of problems.In this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization problems.The main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population matrix.Therefore,in AMBO,any member of the population can play a role in updating the population matrix.The theory of AMBO is described and then mathematically modeled for implementation on optimization problems.The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions.In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also implemented.The optimization results demonstrate the ability of AMBO to solve various optimization problems.Also,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

关键词： Algorithm all members optimization optimization algorithm optimization problem population-based algorithm

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Archery Algorithm:A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

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Computers, Materials & Continua 2022年第7期72卷 399-416页

作者： Fatemeh Ahmadi Zeidabadi mohammad dehghani Pavel Trojovsky Štěpán Hubálovsky Victor Leiva Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic School of Industrial Engineering Pontificia Universidad Católica de ValparaísoValparaíso2362807Chile Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding a suitable solution to an optimization problem designed in science is a major challenge.Therefore,these must be addressed utilizing proper approaches.Based on a random search space,optimization algorithms can find acceptable solutions to problems.Archery Algorithm(AA)is a new stochastic approach for addressing optimization problems that is discussed in this study.The fundamental idea of developing the suggested AA is to imitate the archer’s shooting behavior toward the target panel.The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer.The AA is mathematically described,and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions.Furthermore,the proposed algorithm’s performance is compared vs.eight approaches,including teaching-learning based optimization,marine predators algorithm,genetic algorithm,grey wolf optimization,particle swarm optimization,whale optimization algorithm,gravitational search algorithm,and tunicate swarm algorithm.According to the simulation findings,the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios,and it can give adequate quasi-optimal solutions to these problems.The analysis and comparison of competing algorithms’performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

关键词： Archer meta-heuristic algorithm population-based optimization stochastic programming swarm intelligence population-based algorithm Wilcoxon statistical test

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Graphene oxide based carbon composite as adsorbent for Hg removal:Preparation, characterization, kinetics and isotherm studies

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Chinese Journal of Chemical Engineering 2017年第9期25卷 1170-1175页

作者： Tayebeh Esfandiyari Navid Nasirizadeh mohammad dehghani mohammad Hassan Ehrampoosh Department of Environmental Health Engineering Shaheed Sadoughi University of Medical Sciences Young Researchers and Elite Club Yazd Branch Islamic Azad University

The presence of Hg in the aqueous media is known to cause severe health issues in both humans and animals.Many technologies and especially adsorbents have been applied for its removal. In this study, a graphene oxide–carbon composite(GO–CC) as a new adsorbent was prepared by sol gel procedure and characterized using field emission scanning electron microscopy, BET and EDX. The effects of different variables including solution p H, contact time, adsorbent dose and GO ratio in adsorbent matrix on the removal capacity of Hg were studied. The isotherm data correlated well with the Langmuir isotherm model. Further analysis recommended that the Hg^(2+) adsorption process is governed by the intra-particle and external mass transfer, in which the film diffusion was the rate restrictive step. The presented composite has maximum absorption capacity, q_(max) of 68.8 mg·g^(-1), which is comparable with carbon based adsorbent reported in the previous publications.

关键词： Adsorbent Mercury removal Sol gel Carbon composite

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Ab initio investigation of the structural and unusual electronic properties of α-CuSe (klockmannite)

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Chinese Physics B 2013年第12期22卷 402-409页

作者： Ali Reza Shojaei Zahra Nourbakhsh Aminollah Vaez mohammad dehghani Physics Department Faculty of Science University of Isfahan Physics Department Technical and Vocational University-Mohajer Center

In this article, a computational analysis has been performed on the structural properties and predominantly on the electronic properties of the α-CuSe （klockmannite） using density functional theory. The studies in this work show that the best structural results, in comparison to the experimental values, belong to the PBEsol-GGA and WC-GGA functionals. However, the best results for the bulk modulus and density of states （DOSs） are related to the local density approximation （LDA） functional. Through utilized approaches, the LDA is chosen to investigate the electronic structure. The results of the electronic properties and geometric optimization of α-CuSe respectively show that this compound is conductive and non-magnetic. The curvatures of the energy bands crossing the Fermi level explicitly reveal that major charge carriers in CuSe are holes, whose density is estimated to be 0.86×1022 hole/cm3. In particular, the Fermi surfaces in the first Brillouin zone demonstrate interplane conductivity between （001） planes. Moreover, the charge carriers among them are electrons and holes simultaneously. The conductivity in CuSe is mainly due to the hybridization between the d orbitals of Cu atoms and the p orbitals of Se atoms. The former orbitals have the dual nature of localization and itinerancy.

关键词： klockmannite α-CuSe structural properties electronic properties

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A New Method for Clustering Based on Development of Imperialist Competitive Algorithm

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China Communications 2014年第12期11卷 54-61页

作者： mohammad Reza dehghani Zadeh mohammad Fathian mohammad Reza Gholamian School of Industrial Engineering Iran University of Science and Technology Narmak 16844 Tehran Iran

Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering.In this study,the Imperialist Competitive Algorithm(ICA) is developed and then used in the clustering process.Clustering of IRIS,Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA,GA and PSO algorithms,demonstrate the improvement of Imperialist competitive algorithm.

关键词：聚类算法竞争算法主义开发 K-means算法数据挖掘技术 PSO算法 ICA

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