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A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems

机译：求解粒子群优化问题的基于粒子群算法的混合协同进化文化算法

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摘要

Intelligent evolutionary algorithms have been widely used to solve large-scale, complex global optimization problems. Co-evolutionary algorithm (CEA), cultural algorithm (CA), and particle swarm optimization (PSO) are all promising methods in the field of intelligent computation. In this paper, a hybrid co-evolutionary cultural algorithm based on particle swarm optimization (CECBPSO) is proposed. In CECBPSO, a novel space called shared global belief space (SGBS) is introduced into the co-evolutionary mechanism, and a new co-evolutionary cultural framework is built. Through the synergistic mechanism, the algorithm has higher probability of avoiding local optima and the whole swarm can find global optima more quickly. Factorial Design (FD) approach is used in this paper in order to get a guideline on how to tune the designed parameters in CECBPSO. Extensive computational studies are also carried out to evaluate the performance of CECBPSO on thirteen benchmark functions and three real-life optimization problems. The results show that the proposed algorithm has superior performance to other compared algorithms in terms of accuracy and convergence speed, especially on high-dimensional problems.
机译：智能进化算法已被广泛用于解决大规模，复杂的全局优化问题。协同进化算法（CEA），文化算法（CA）和粒子群优化（PSO）在智能计算领域都是有前途的方法。提出了一种基于粒子群优化算法的混合协同进化文化算法。在CECBPSO中，将一种称为共享全球信念空间（SGBS）的新颖空间引入到协同进化机制中，并建立了一个新的协同进化文化框架。通过协同机制，该算法避免局部最优的可能性更高，整个群体可以更快地找到全局最优。本文使用阶乘设计（FD）方法来获得有关如何在CECBPSO中调整设计参数的指南。还进行了广泛的计算研究，以评估CECBPSO在13个基准函数和3个实际优化问题上的性能。结果表明，该算法在准确性和收敛速度方面，特别是在高维问题上，具有优于其他算法的性能。

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  • 来源
    《Neurocomputing》 |2012年第2012期|p.76-89|共14页
  • 作者

    Yang Sun; Lingbo Zhang; Xingsheng Gu;

  • 作者单位

    Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China;

    Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China;

    Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    co-evolutionary algorithm; cultural algorithm; particle swarm optimization; global optimization; factorial design; orthogonal test;

    机译：协同进化算法;文化算法;粒子群优化;全局优化析因设计;正交检验;

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