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A New metaheuristic Approach to Solving Benchmark Problems: Hybrid Salp Swarm Jaya Algorithm

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Computers, Materials & Continua 2022年第5期71卷 2923-2941页

作者： Erkan Erdemir Adem Alpaslan Altun Department of Information Technologies Tokat Vocational and Technical Anatolian High SchoolMerkez/Tokat60030Turkey Department of Computer Engineering Faculty of TechnologyKonya Selcuk UniversitySelcuklu/Konya42130Turkey

metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost.As with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms.In this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is designed.The speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms.The hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution part.HSSJAYA works according to the best and worst food source positions.In this way,it is thought that the leader-follower salps will find the best solution to reach the food source.The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions.The results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained.In addition,these results were statistically compared using wilcoxon rank sum test with other algorithms.According to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.

关键词： metaheuristic optimization benchmark algorithm swarm hybrid

Rock Hyraxes Swarm Optimization: A New Nature-Inspired metaheuristic Optimization Algorithm

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Computers, Materials & Continua 2021年第7期68卷 643-654页

作者： Belal Al-Khateeb Kawther Ahmed Maha Mahmood Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarRamadiIraq General Directorate of Scientific Welfare Ministry of Youth and SportBaghdadIraq Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food.Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group.Forty-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests.Further,RHSO is very effective in solving real issues with constraints and new search space.It is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.

关键词： Optimization metaheuristic constrained optimization rock hyraxes swarm optimization RHSO

MOALG: A metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

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Computers, Materials & Continua 2024年第3期78卷 3489-3510页

作者： Rashmi Sharma Ashok Pal Nitin Mittal Lalit Kumar Sreypov Van Yunyoung Nam Mohamed Abouhawwash Department of Mathematics Chandigarh UniversityGharuanMohali140413India Deaprtment of Industry 4.0 Shri Vishwakarma Skill UniversityPalwal121102India Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Computational Mathematics Science and Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI 48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

关键词： Multi-objective optimization genetic algorithm ant lion optimizer metaheuristic

Dark Forest Algorithm:A Novel metaheuristic Algorithm for Global Optimization Problems

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Computers, Materials & Continua 2023年第5期75卷 2775-2803页

作者： Dongyang Li Shiyu Du Yiming Zhang Meiting Zhao Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo315000China Engineering Laboratory of Advanced Energy Materials Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo315000China School of Material and Chemical Engineering Ningbo UniversityNingbo315000China

metaheuristic algorithms,as effective methods for solving optimization problems,have recently attracted considerable attention in science and engineering fields.They are popular and have broad applications owing to their high efficiency and low complexity.These algorithms are generally based on the behaviors observed in nature,physical sciences,or humans.This study proposes a novel metaheuristic algorithm called dark forest algorithm(DFA),which can yield improved optimization results for global optimization problems.In DFA,the population is divided into four groups:highest civilization,advanced civilization,normal civilization,and low civilization.Each civilization has a unique way of iteration.To verify DFA’s capability,the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms,including artificial bee colony algorithm,firefly algorithm,grey wolf optimizer,harmony search algorithm,grasshopper optimization algorithm,and whale optimization algorithm.The results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional problems.DFAis applied to five engineering projects to demonstrate its applicability.The results show that the performance of DFA is competitive to that of current well-known metaheuristic algorithms.Finally,potential upgrading routes for DFA are proposed as possible future developments.

关键词： metaheuristic algorithm global optimization

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

On metaheuristic Optimization Motivated by the Immune System

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Applied Mathematics 2014年第2期5卷 318-326页

作者： Mohammed Fathy Elettreby Elsayd Ahmed Houari Boumedien Khenous Department of Mathematics Faculty of Science King Khalid University Abha KSA Department of Mathematics Faculty of Science King Khalid University Abha KSA Department of Mathematics Faculty of Science Mansoura University Mansoura Egypt Department of Mathematics Faculty of Science Mansoura University Mansoura Egypt

In this paper, we modify the general-purpose heuristic method called extremal optimization. We compare our results with the results of Boettcher and Percus [1]. Then, some multiobjective optimization problems are solv... 详细信息

关键词： Multiobjective Optimization Extremal Optimization Immunememory and metaheuristic

Research Advances on AI-Powered Thermal Management for Data Centers

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Tsinghua Science and Technology 2022年第2期27卷 303-314页

作者： Hui Liu AbdusSalam Aljbri Jie Song Jinqing Jiang Chun Hua the School of Metallurgy Northeastern UniversityShenyang 110819China the Software College Northeastern UniversityShenyang 110169China the College of Computer Science and Technology Inner Mongolia University for NationalitiesTongliao 010021China

Given the complex nature of data centers’thermal management,which costs too many resources,processing time,and energy consumption,thermal awareness and thermal management powered by artificial intelligence(AI)are the targeted study.In addition to a few research on AI techniques and models,other strategies have also been introduced in recent years.Data center models,including cooling,thermal,power,and workload models,and their relationship are factors that need to be understood in the optimal thermal management system.Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines(VMs),hotspot identification,thermal state estimation,and power usage change.AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening.At present,research on AI-powered thermal control is still in its infancy.This paper concludes with four issues in thermal management,which will be the scope of further research.

关键词： data center thermal management artificial intelligence heuristic metaheuristic

Wind Driven Butterfly Optimization Algorithm with Hybrid Mechanism Avoiding Natural Enemies for Global Optimization and PID Controller Design

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Journal of Bionic Engineering 2023年第6期20卷 2935-2972页

作者： Yang He Yongquan Zhou Yuanfei Wei Qifang Luo Wu Deng College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China College of Electronic Information and Automation Civil Aviation University of ChinaTianjin300300China Faculty of Information Science and Technology Universiti Kebangsaan Malaysia43600BangiSelangorMalaysia Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabilities,the butterfly actions were divided into downwind and upwind states.The algorithm of exploration ability was improved with the wind,while the algorithm of exploitation ability was improved against the wind.Also,a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching ability.Aiming at improving the explorative performance at the initial stages and later stages,the fragrance generation method was modified.To evaluate the effectiveness of the suggested algorithm,a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark functions.Further,the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC 2020.Finally,the WDBOA algorithm is used proportional-integral-derivative(PID)controller parameter optimization.Experimental results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm(GA),Flower Pollination Algorithm(FPA),Cuckoo Search(CS)and BOA.

关键词： Butterfly Optimization Algorithm(BOA) Wind Driven Optimization(WDO) Benchmark functions Global optimization Proportional integral derivative(PID) metaheuristic

A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

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Computers, Materials & Continua 2024年第5期79卷 2277-2293页

作者： Zhigao Zeng Aoting Tang Shengqiu Yi Xinpan Yuan Yanhui Zhu School of Computer Hunan University of TechnologyZhuzhou412007China Hunan Key Laboratory of Intelligent Information Perception and Processing Technology Zhuzhou412007China

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.

关键词： Radiomics feature selection machine learning metaheuristic