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Enhanced Moth-flame Optimization Based on Cultural Learning and gaussian mutation

Journal of Bionic Engineering 2018年第4期15卷 751-763页

作者： Liwu Xu Yuanzheng Li Kaicheng Li Gooi Hoay Beng Zhiqiang Jiang Chao Wang Nian Liu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan 430074 China School of Automation Ministry of Education Key Laboratory of lmage Processing and Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China Nanyang Technological University Singapore 639798 Singapore School of Hydropower and lnformation Engineering Huazhong University of Science and Technology Wuhan 430074 China China Institute of Water Resources and Hydropower Research Beijing 100038 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beij'ing 102206 China

This paper presents an Enhanced Moth-Flame Optimization （EMFO） technique based on Cultural Learning （CL） and gaussian mutation （GM）. The mechanism of CL and the operator of GM are incorporated to the original algorithm of Moth-Flame Optimization （MFO）. CL plays an important role in the inheritance of historical experiences and stimulates moths to obtain information from flames more effectively, which helps MFO enhance its searching ability. Furthermore, in order to overcome the disadvantage of trapping into local optima, the operator of GM is introduced to MFO. This operator acts on the best flame in order to generate several variant ones, which can increase the diversity. The proposed algorithm of EMFO has been comprehensively evaluated on 13 benchmark functions, in comparison with MFO. Simulation results verify that EMFO shows a significant improvement on MFO, in terms of solution quality and algorithmic reliability.

关键词： bioinspired computing moth-flame optimization cultural learning gaussian mutation benchmark functions

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Improving NSGA-Ⅱ by a Local Search Strategy with gaussian mutation

Improving NSGA-Ⅱ by a Local Search Strategy with Gaussian M...

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第40届中国控制会议

作者： Zhijun Zhang Baiquan Lu School of Mechatronic Engineering and Automation Shanghai University

An improved non-dominated sorting genetic algorithm NSGA-Ⅱ-GLS is proposed in this paper, which intends to enhance the local search ability of the original NSGA-Ⅱ and reduce the likelihood of falling into local optimal. The proposed algorithm NSGA-Ⅱ-GLS introduces a gaussian mutation operator into the genetic operation in the algorithm, which makes the individuals focus on exploiting the space near themselves. Also, the improved algorithm adopts a jiggling local search strategy in order to jump out of non-global Pareto fronts. The performance of NSGA-Ⅱ-GLS is compared with NSGA-Ⅱ and NSGA-Ⅱ-DE through five DTLZ test problems. The simulation results indicate that the NSGA-Ⅱ-GLS has a good local search capability and the convergence and diversity of Pareto optimal solutions from NSGA-Ⅱ-GLS are better than the original NSGA-Ⅱ. In addition,solutions generated by NSGA-Ⅱ-GLS suggest that NSGA-Ⅱ-GLS is more versatile than NSGA-Ⅱ-DE.

关键词： Multi-objective optimization NSGA-Ⅱ gaussian mutation Local search strategy Pareto optimal

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An improved moth-flame optimization algorithm with periodic mutation and gaussian mutation for safety-enhanced UAV path planning

An improved moth-flame optimization algorithm with periodic ...

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第43届中国控制会议

作者： Xiaodong Zhao Zhiqiang Hu Xiaoqian Li Xianliang Zhang School of Mathematics and Statistics Taishan University

To handle the path planning problem of unmanned aerial vehicles(UAV) meeting numerous obstacles in complicated environments, an improved moth-flame optimization(MFO) algorithm with periodic and gaussian mutations(PGMFO) is proposed. For PGMFO, firstly, moths are able to jump out of local optima and better their position by means of periodic ***, the gaussian mutation is used to select the optimal flame in each iteration can improve the development capability of MFO. Finally, PGMFO is contrasted with other 9 optimization algorithms using 23 test problems. According to the findings,PGMFO performs faster and more accurately during convergence in the majority of optimization issues. In addition, in the generation of benchmark scenes in real digital elevation model maps, PGMFO is used to solve the optimization problem of feasible and safe operation requirements and constraints for UAVs. The results show that the validity of the PGMFO.

关键词： Moth-flame optimization algorithm periodic mutation gaussian mutation UAV path planning

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FORM ERROR EVALUATION OF CIRCLES BASED ON A FINELY-DESIGNED GENETIC ALGORITHM

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Chinese Journal of Mechanical Engineering 2004年第1期17卷 59-62页

作者： CuiChangcai CheRensheng LiZhongyan YeDong DepartmentofAutomaticMeasurementandControl HarbinInstituteofTechnologyHarbin150001China

A genetic algorithm(GA)-based new method is designed to evaluate thecircularity error of mechanical parts. The method uses the capability of nonlinear optimization ofGA to search for the optimal solution of circularity error. The finely-designed GA (FDGA)characterized dynamical bisexual recombination and gaussian mutation. The mathematical model of thenonlinear problem is given. The implementation details in FDGA are described such as the crossoveror recombination mechanism which utilized a bisexual reproduction scheme and the elitist reservationmethod; and the adaptive mutation which used the gaussian probability distribution to determine thevalues of the offspring produced by mutation mechanism. The examples are provided to verify thedesigned FDGA. The computation results indicate that the FDGA works very well in the field of formerror evaluation such as circularity evaluation.

关键词： Genetic algorithm(GA) Form error Circularity Bisexual recombination gaussian mutation

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Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

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Complex System Modeling and Simulation 2022年第4期2卷 288-306页

作者： Wei Li Yangtao Chen Qian Cai Cancan Wang Ying Huang Soroosh Mahmoodi the School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China the School of Mathematical and Computer Science Gannan Normal UniversityGanzhou 341000China the Soroosh Khorshid Iranian Co. Abyek Industrial ZoneQazvin 999067Iran

Particle swarm optimization(PSO)is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of ***,PSO still has certain deficiencies,such as a poor trade-off between exploration and exploitation and premature ***,this paper proposes a dual-stage hybrid learning particle swarm optimization(DHLPSO).In the algorithm,the iterative process is partitioned into two *** learning strategy used at each stage emphasizes exploration and exploitation,*** the first stage,to increase population variety,a Manhattan distance based learning strategy is *** this strategy,each particle chooses the furthest Manhattan distance particle and a better particle for *** the second stage,an excellent example learning strategy is adopted to perform local optimization operations on the population,in which each particle learns from the global optimal particle and a better *** the gaussian mutation strategy,the algorithm’s searchability in particular multimodal functions is significantly *** benchmark functions from CEC 2013,DHLPSO is evaluated alongside other PSO variants already in *** comparison results clearly demonstrate that,compared to other cutting-edge PSO variations,DHLPSO implements highly competitive performance in handling global optimization problems.

关键词： particle swarm optimization Manhattan distance example learning gaussian mutation dual-stage global optimization problem

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Magnetic Flux Leakage Signal Inversion of Corrosive Flaws Based on Modified Genetic Local Search Algorithm

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Journal of Shanghai Jiaotong university(Science) 2009年第2期14卷 168-172页

作者：韩文花杨平夏飞薛阳 Department of Information and Controlling Engineering Shanghai University of Electric Power

In this paper,a modified genetic local search algorithm(MGLSA) is *** proposed algorithm is resulted from employing the simulated annealing technique to regulate the variance of the gaussian mutation of the genetic local search algorithm(GLSA).Then,an MGLSA-based inverse algorithm is proposed for magnetic flux leakage(MFL) signal inversion of corrosive flaws,in which the MGLSA is used to solve the optimization problem in the MFL inverse *** results demonstrate that the MGLSA-based inverse algorithm is more robust than GLSA-based inverse algorithm in the presence of noise in the measured MFL signals.

关键词： magnetic flux leakage (MFL) corrosive flaw simulated annealing gaussian mutation

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Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

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Financial Innovation 2019年第1期5卷 359-386页

作者： Massimiliano Kaucic Mojtaba Moradi Mohmmad Mirzazadeh Department of Economics BusinessMathematics and StatisticsUniversity of TriestePiazzale Europa 134127 TriesteItaly Department of Industrial Engineering Faculty of Technology and EngineeringEast of GuilanUniversity of Guilan44891-63157 Rasar-VajargahIran Department of Engineering Sciences Faculty of Technology and EngineeringEast of GuilanUniversity of Guilan44891-63157 Rasar-VajargahIran.

In this study,we analyze three portfolio selection strategies for loss-averse investors:semi-variance,conditional value-at-risk,and a combination of both risk ***,we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization *** effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available *** computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance ***,they are able to approximate the Pareto front even in cases in which all the other approaches fail.

关键词： Multi-objective portfolio optimization Semi-variance CVaR NSGA-II SPEA 2 Intermediate crossover gaussian mutation

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Area Traffic Signal Optimization Based on Adaptive PSO with mutation

Area Traffic Signal Optimization Based on Adaptive PSO with ...

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2009年中国智能自动化会议

作者： MA Ling-ling,DAI Ya-ping,LIU Yan,DAI Zhong-jian (School of Automation,Beijing Institute of Technology,Beijing100081,China)

This paper applies adaptive Particle Swarm Optimization(PSO) to traffic signal timing *** is used as a search tool to find the optimal time allocation schemes for phase signal of *** results show that the algorithm pr... 详细信息

关键词： Area traffic signal optimization Adaptive PSO gaussian mutation

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