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Dipper Throated optimization Algorithm for Unconstrained function and Feature Selection

Computers, Materials & Continua 2022年第7期72卷 1465-1481页

作者： Ali E.Takieldeen El-Sayed M.El-kenawy Mohammed Hadwan Rokaia M.Zaki Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Higher Institute of Engineering and Technology KafrelsheikhEgypt Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm optimization(PSO),Whale optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.

关键词： Metaheuristic optimization swarmoptimization feature selection function optimization

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Adaptive immune-genetic algorithm for global optimization to multivariable function

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Journal of Systems Engineering and Electronics 2007年第3期18卷 655-660页

作者： Dai Yongshou Li Yuanyuan Wei Lei Wang Junling Zheng Deling Coll. of Information and Control Engineering China Univ. of Petroleum Dongying 257061 P. R. China School of Information Engineering Univ. of Science and Technology Beijing Beijing 100083 P. R. China

An adaptive immune-genetic algorithm （AIGA） is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response （secondary response）, adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.

关键词： immune-genetic algorithm function optimization hyper-mutation density operator.

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An Improved Harris Hawk optimization Algorithm

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Mechanical Engineering Science 2024年第1期6卷 21-25页

作者： GuangYa Chong Yongliang YUAN He’nan Polytechnic University JiaozuoHe’nan454150China

Aiming at the problems that the original Harris Hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum,this paper proposes an improved Harris Hawk optimization algorithm(GHHO).Firstly,we used a Gaussian chaotic mapping strategy to initialize the positions of individuals in the population,which enriches the initial individual species characteristics.Secondly,by optimizing the energy parameter and introducing the cosine strategy,the algorithm's ability to jump out of the local optimum is enhanced,which improves the performance of the algorithm.Finally,comparison experiments with other intelligent algorithms were conducted on 13 classical test function sets.The results show that GHHO has better performance in all aspects compared to other optimization algorithms.The improved algorithm is more suitable for generalization to real optimization problems.

关键词： Harris Hawk optimization algorithm chaotic mapping cosine strategy function optimization

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A Parallel Global-Local Mixed Evolutionary Algorithm for Multimodal function optimization Based on Domain Decomposition

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Wuhan University Journal of Natural Sciences 2003年第S1期8卷 253-258页

作者： Wu Zhi-jian, Tang Zhi-long,Kang Li-shanState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, China

This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.

关键词： function optimization GT algorithm GLME algorithm evolutionary algorithm domain decomposition

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function optimization Based on Improved Clonal Selection Algorithm

Function Optimization Based on Improved Clonal Selection Alg...

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第14届中国系统仿真技术及其应用学术年会

作者： Xi CHEN 1,Yuxuan DING 2,Jianjun HE 3 1 School of Information and Communication Engineering,Hunan Institute of Science and Technology,Yueyang,Hunan,China 2 College of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan,China 3 School of Information Science and Engineering,Central South University,Changsha Hunan,China

An improved clonal selection algorithm is proposed for the two shortcomings of the traditional algorithm.Improved contents are as follows:An adaptive improved mutation operator is designed.And we put forward an improved selection operator which takes into account two factors of affinity and concentration.The simulation results show the improved algorithm is superior to the traditional clonal selection algorithm in convergence speed and optimizing result.

关键词： Clonal selection algorithm affinity weight factor convergence speed function optimization

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Improved Differential Evolution Algorithm and its Application in Complex function optimization

Improved Differential Evolution Algorithm and its Applicatio...

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第26届中国控制与决策会议

作者： XiaoGang Dong Yan Liu ChangShou Deng School of Information Science and Technology Jiujiang University

When solving complex function optimization problem, Differential evolution（DE） algorithms may suffer from low convergence rate. In this paper, we propose an improved differential evolution algorithm named n-IDE. Our algorithm uses Gaussian sequence to dynamically generate zoom factors and applies an improved hybrid mutation strategy to individuals in order to improve the overall performance. We compare n-IDE with existing DE approaches using benchmark functions and the experimental result shows that n-IDE has significant improvement on the convergence rate.

关键词： function optimization Differential evolution Hybrid Mutation Gaussian sequence

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A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction

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Frontiers of Information Technology & Electronic Engineering 2023年第11期24卷 1574-1590页

作者： Shaoqiang YE Kaiqing ZHOU Azlan Mohd ZAIN Fangling WANG Yusliza YUSOFF School of Communication and Electronic Engineering Jishou UniversityJishou 416000China Faculty of Computing Universiti Teknologi MalaysiaSkudai 81310JohorMalaysia

Harmony search(HS)is a form of stochastic meta-heuristic inspired by the improvisation process of musicians.In this study,a modified HS with a hybrid cuckoo search(CS)operator,HS-CS,is proposed to enhance global search ability while avoiding falling into local optima.First,the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization.This is to improve the efficiency and accuracy of HS algorithm optimization.Second,the CS operator is introduced to expand the scope of the solution space and improve the density of the population,which can quickly jump out of the local optimum in the randomly generated harmony and update stage.Finally,a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization.Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm.In addition,12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS.The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness,high convergence speed,and high convergence accuracy.For further verification,HS-CS is used to optimize the back propagation neural network(BPNN)to extract weighted fuzzy production rules.Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules.Therefore,the proposed HS-CS is proved to be effective.

关键词： Harmony search algorithm Cuckoo search algorithm Global convergence function optimization Weighted fuzzy production ruleextraction

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Adaptive Simplified Chicken Swarm optimization Based on Inverted S-Shaped Inertia Weight

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Chinese Journal of Electronics 2022年第2期31卷 367-386页

作者： GU Yanchun LU Haiyan XIANG Lei SHEN Wanqiang School of Science Jiangnan University Wuxi Engineering Technology Research Center for Biological Computing Jiangnan University

Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic chicken swarm optimization algorithm, an adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight(ASCSO-S) is proposed. Firstly, a simplified chicken swarm optimization algorithm is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the hens.Simulation experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.

关键词： Chicken swarm optimization algorithm Inverted S-shaped inertia weight Random learning function optimization Richards model

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Convergence Track Based Adaptive Differential Evolution Algorithm(CTbADE)

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Computers, Materials & Continua 2022年第7期72卷 1229-1250页

作者： Qamar Abbas Khalid Mahmood Malik Abdul Khader Jilani Saudagar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Engineering Oakland UniversityRochesterMIUSA Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research paper.The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima problem.Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems.An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper.The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE algorithm.Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE algorithms.This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

关键词： Differential evolution function optimization convergence track parameter sequence adaptive control parameters

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A Bionic optimization Technique with Cockroach Biological Behavior

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Chinese Journal of Electronics 2021年第4期30卷 644-651页

作者： CHENG Le CHANG Lyu SONG Yanhong WANG Haibo XU Yihan BIAN Yuetang College of Computer Science and Communication Jiangsu Vocational College of Electronics and Information College of Computer and Information Hohai University School of Business Nanjing Normal University

Many practical engineering problems can be abstracted as corresponding function optimization problems. During the last few decades, many bionic algorithms have been proposed for this problem. However,when optimizing for large scale problems, such as 1000 dimensions, many existing search techniques may no longer perform well. Inspired by the social model of cockroaches,this paper presents a novel search technique called Cooperation cockroach colony optimization(CCCO). In the CCCO algorithm, two kinds of special biological behavior of cockroach, wall-following and nest-leaving,are simulated and the whole population is divided into wall-following and nest-leaving populations. By the collaboration of the two populations, CCCO accomplishes the computation of global optimization. The crucial parameters of CCCO are set by the self-adaptive method.Moreover, a discussion on group model design is provided in this paper. The CCCO algorithm is evaluated with shifted test functions(1000 dimensions). Three state-ofthe-art cockroach-inspired algorithms are used for the comparative experiments. Furthermore, CCCO is applied to a real-world optimization problem concerning spread spectrum radar poly-phase. Experiment results show that the CCCO algorithm can be applied to optimize large-scale problems with the good performance.

关键词： function optimization Cooperation cockroach colony optimization Wall-following Nestleaving

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