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The Markov Chain Analysis of premature convergence of Genetic Algorithms

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Chinese Quarterly Journal of Mathematics 2003年第4期18卷 364-368页

作者：赵小艳聂赞坎 Faculty of Science Xi'an Jiaotong University Xi'an 710049

This paper discussed CGA population Markov chain with mutation probability. For premature convergence of this algorithm, one concerned, we give its analysis of Markov chain.

关键词： genetic algorithm premature convergence uniform population

Chaotic migration-based pseudo parallel genetic algorithm and its application in inventory optimization

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Journal of Systems Engineering and Electronics 2005年第2期16卷 411-417页

作者： ChenXiaofang GuiWeihua WangYalin SchoolofInformationScience&Engineering CentralSouthUniversityChangsha410083P.R.China

Considering premature convergence in the searching process of genetic algorithm, a chaotic migration-based pseudo parallel genetic algorithm (CMPPGA) is proposed, which applies the idea of isolated evolution and information exchanging in distributed Parallel Genetic Algorithm by serial program structure to solve optimization problem of low real-time demand. In this algorithm, asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence. Information exchanging among sub-populations is ensured to be efficient and sufficient due to that the sequence is ergodic and stochastic. Simulation study of CMPPGA shows its strong global search ability, superiority to standard genetic algorithm and high immunity against premature convergence. According to the practice of raw material supply, an inventory programming model is set up and solved by CMPPGA with satisfactory results returned.

关键词： parallel genetic algorithm chaos premature convergence inventory optimization.

Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第8期15卷 687-696页

作者： De-xuan ZOU Li-qun GAO Steven LI School of Electrical Engineering and Automation Jiangsu Normal University School of Information Science and Engineering Northeastern University Graduate School of Business and Law RMIT UniversityGPO Box 2476 Melbourne 3001 Australia

This paper presents a ranked differential evolution(RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.

关键词： Ranked differential evolution Identification problem Nonlinear discrete-time systems Volterra filter model premature convergence

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

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Computers, Materials & Continua 2024年第2期78卷 2337-2360页

作者： Zhaolin Lv Yuexia Zhao Hongyue Kang Zhenyu Gao Yuhang Qin College of Systems Engineering National University of Defense TechnologyChangsha410073China College of Information Management Nanjing Agricultural UniversityNanjing210031China Department of Intelligent Supply Chain JD LogisticsBeijing100076China College of Intelligent Science and Technology National University of Defense TechnologyChangsha410073China

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production *** Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling ***,HHO suffers from premature convergence when solving NP-hard ***,this paper proposes an improved HHO algorithm(GNHHO)to solve the *** introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the *** paper chooses a two-segment encoding mode based on the job and the machine of the *** verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard ***,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s *** optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic ***,it achieves an average increase of 21.50%in the on-time order delivery *** results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

关键词： Flexible job shop scheduling improved Harris hawk optimization algorithm(GNHHO) premature convergence maximum completion time(makespan)

Improved genetic operator for genetic algorithm

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Journal of Zhejiang University Science 2002年第4期3卷 431-434页

作者：林峰杨启文 CollegeofElectricalEngineering ZhejiangUniversityHangzhou310027China

The mutation operator has been seldom improved because researchers hardly suspect its ability to prevent genetic algorithm (GA) from converging prematurely. Due to its importance to GA, the authors of this paper study its influence on the diversity of genes in the same locus, and point out that traditional mutation, to some extent, can result in premature convergence of genes (PCG) in the same locus. The above drawback of the traditional mutation operator causes the loss of critical alleles. Inspired by digital technique, we introduce two kinds of boolean operation into GA to develop a novel mutation operator and discuss its contribution to preventing the loss of critical alleles. The experimental results of function optimization show that the improved mutation operator can effectively prevent premature convergence, and can provide a wide selection range of control parameters for GA.

关键词： Genetic algorithm(GA) Mutation operator premature convergence

DV-Hop Localization Algorithm with Weight-based Oriented Cuckoo Search Algorithm 36

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DV-Hop Localization Algorithm with Weight-based Oriented Cuc...

Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

第36届中国控制会议

作者： Maoqing Zhang Zhuanghua Zhu Zhihua Cui Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi Finance & Taxation College

ISBN: (纸本)9781538629185

Oriented cuckoo search algorithm（OCS） is a newly proposed variant of cuckoo search algorithm,to avoid the premature convergence,weighted-based oriented cuckoo search（WOCS） is proposed which is a combination of OCS and the standard *** achieves a balance between exploration and *** test the performance,WOCS is incorporated into the methodology of DV-Hop localization algorithm for node localization in wireless sensor *** simulation results show that DV-Hop localization with WOCS achieves best positioning accuracy when compared with other two algorithms.

关键词： DV-Hop localization algorithm Wireless sensor network Weighted-based oriented cuckoo search premature convergence Positioning accuracy

来源： cnki会议评论

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Intelligent Automation & Soft Computing 2023年第9期37卷 2783-2807页

作者： Dongping Tian Bingchun Li Jing Liu Chen Liu Ling Yuan Zhongzhi Shi School of Computer Science and Technology Kashi UniversityKashi844006China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence ***,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal *** this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the ***,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution *** be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution *** by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm *** addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO ***,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

关键词： Particle swarm optimization local optima acceleration coefficients swarm diversity premature convergence

Pattern synthesis of antennas based on a modified particle swarm optimization algorithm

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Frontiers of Electrical and Electronic Engineering in China 2007年第4期2卷 454-458页

作者： JIN Ronghong YUAN Zhihao GENG Junping FAN Yu LI Jiajing Department of Electronic Engineering Shanghai Jiaotong UniversityShanghai 200030China

In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as global best perturbation and inertia weight jump threshold are *** convergence speed and accuracy of the algo-rithm are *** test by some benchmark problems shows that the proposed algorithm achieves relatively higher ***,the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented.

关键词： particle swarm optimization(PSO)algorithm premature convergence array antennas patterns synthesis

Particle Swarm Optimization with Adaptive Mutation

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Frontiers of Electrical and Electronic Engineering in China 2006年第1期1卷 99-104页

作者： LU Zhen-su HOU Zhi-rong DU Juan School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China

A new adaptive mutation particle swarm optimizer,which is based on the variance of the population's fitness,is presented in this *** the running time,the mutation probability for the current best particle is determined by two factors:the variance of the population's fitness and the current optimal *** ability of particle swarm optimization(PSO)algorithm to break away from the local optimum is greatly improved by the *** experimental results show that the new algorithm not only has great advantage of convergence property over genetic algorithm and PSO,but can also avoid the premature convergence problem effectively.

关键词： particle swarm adaptive mutation optimization premature convergence