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面向多目标优化问题的基于Species的遗传算法 被引量:1

Species-Based Genetic Algorithm for Multiobjective Optimization Problems
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摘要 为了能够快速准确地获得多目标优化问题的一组非支配解,提出了一种基于Species的多目标遗传算法.该算法采用Tchebycheff方法构建一定数量的子问题,进而基于Species机制构造多种群实现了对多个子问题的并行求解.这种采用多个体对一个最优解的搜索方式提高了算法的探索能力和开发能力.最后,对一组标准测试函数进行仿真实验,结果表明所提出的算法能够快速准确地获得一定数量的非支配解. In order to achieve a set of nondominated solutions for multiobjective optimization problems quickly and accurately, a Species-based genetic algorithm for multiobjecitve optimization problems was proposed. Firstly, a certain number of subproblems were developed with the Tchebycheff approach. Then multiple subpopulations were constructed based on the Species mechanism to solve all the subproblems simultaneously, which can improve the exploration and exploitation ability by using multiple individuals to search one optimal solution. Finally, a set of benchmark multiobjective functions were examined, and the experimental results showed that the proposed algorithm can obtain a certain number of nondominated solutions quickly and accurately.
出处 《东北大学学报(自然科学版)》 EI CAS CSCD 北大核心 2016年第3期314-318,共5页 Journal of Northeastern University(Natural Science)
基金 国家杰出青年科学基金资助项目(71325002 61225012) 国家自然科学基金资助项目(71071028 71001018) 流程工业综合自动化国家重点实验室基础科研业务费资助项目(2013ZCX11) 中央高校基本科研业务费专项资金资助项目(N130404017)
关键词 多目标优化问题 遗传算法 多目标优化算法 Species机制 Tchebycheff方法 multiobjective optimization problem genetic algorithm multiobjective optimization algorithm Species mechanism Tchebycheff approach
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参考文献12

  • 1Zhou A,Qu B,Li H,et al.Multiobjective evolutionary algorithms:a survey of the state of the art[J].Swarm and Evolutionary Computation,2011,1(1):32-49.
  • 2Deb K,Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197.
  • 3Qu B Y,Suganthan P N.Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection[J].Information Sciences,2010,180(17):3170-3181.
  • 4Zhang Q,Li H.MOEA/D:a multiobjective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation, 2007,11(6):712-731.
  • 5Goh C K,Tan K C.A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization[J].IEEE Transactions on Evolutionary Computation, 2009,13(1):103-127.
  • 6Zhang Y,Gong D,Ding Z.Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer[J].Expert Systems with Applications,2011,38(11):13933-13941.
  • 7Zhan Z H,Li J J,Cao J N,et al.Multiple populations for multiple objectives:a coevolutionary technique for solving multiobjective optimization problems [J].IEEE Transactions on Cybernetics,2013,43(2):445-463.
  • 8Li J P,Balazs M E,Parks G T,et al.A species conserving genetic algorithm for multimodal function optimization[J].Evolutionary Computation,2002,10(3):207-234.
  • 9Wang H,Moon I,Yang S,et al.A memetic particle swarm optimization algorithm for multimodal optimization problems[J].Information Sciences,2012,197:38-52.
  • 10Petalas Y G,Parsopoulos K E,Vrahatis M N.Memetic particle swarm optimization[J].Annals of Operation Research,2007,156(1):99-127.

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