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Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems

机译：基于群体智能的掠夺性搜索策略的连续优化问题

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摘要

We propose an approach to solve continuous variable optimization problems. The approach is based on the integration of predatory search strategy (PSS) and swarm intelligence technique. The integration is further based on two newly defined concepts proposed for the PSS, namely, "restriction" and "neighborhood," and takes the particle swarm optimization (PSO) algorithm as the local optimizer. The PSS is for the switch of exploitation and exploration (in particular by the adjustment of neighborhood), while the swarm intelligence technique is for searching the neighborhood. The proposed approach is thus named PSS-PSO. Five benchmarks are taken as test functions (including both unimodal and multimodal ones) to examine the effectiveness of the PSS-PSO with the seven well-known algorithms. The result of the test shows that the proposed approach PSS-PSO is superior to all the seven algorithms.
机译：我们提出了一种解决连续变量优化问题的方法。该方法基于掠夺性搜索策略（PSS）和群体智能技术的集成。集成还基于为PSS提出的两个新定义的概念，即“约束”和“邻域”，并将粒子群优化（PSO）算法用作本地优化器。 PSS用于切换开发和勘探（特别是通过调整邻域），而群智能技术则用于搜索邻域。因此，所提出的方法称为PSS-PSO。五个基准被用作测试功能（包括单峰和多峰），以使用七种著名算法检查PSS-PSO的有效性。测试结果表明，所提出的方法PSS-PSO优于所有七个算法。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第3期|749256.1-749256.11|共11页
  • 作者

    J. W. Wang; H. F. Wang; W. H. Ip; K. Furuta; T. Kanno; W. J. Zhang;

  • 作者单位

    Complex Systems Research Center, East China University of Science and Technology, Shanghai 200237, China,Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada S7N 5A9,Department of Systems Innovation, the University of Tokyo, Tokyo 113-8656, Japan;

    Institute of Systems Engineering, Northeastern University, Shenyang 110114, China;

    Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong;

    Department of Systems Innovation, the University of Tokyo, Tokyo 113-8656, Japan;

    Department of Systems Innovation, the University of Tokyo, Tokyo 113-8656, Japan;

    Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada S7N 5A9;

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