东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (3): 314-318.DOI: 10.12068/j.issn.1005-3026.2016.03.003

• 信息与控制 • 上一篇    下一篇

面向多目标优化问题的基于Species的遗传算法

付亚平1,2, 王洪峰1,2, 黄敏1,2   

  1. (1. 东北大学 信息科学与工程学院, 辽宁 沈阳110819; 2. 东北大学 流程工业综合自动化国家重点实验室, 辽宁 沈阳110819)
  • 收稿日期:2015-01-05 修回日期:2015-01-05 出版日期:2016-03-15 发布日期:2016-03-07
  • 通讯作者: 付亚平
  • 作者简介:付亚平(1985-),男,山东青岛人,东北大学博士研究生;黄敏(1968-),女,福建长乐人,东北大学教授,博士生导师.
  • 基金资助:
    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).

Species-Based Genetic Algorithm for Multiobjective Optimization Problems

FU Ya-ping1,2, WANG Hong-feng1,2, HUANG Min1,2   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China.
  • Received:2015-01-05 Revised:2015-01-05 Online:2016-03-15 Published:2016-03-07
  • Contact: WANG Hong-feng
  • About author:-
  • Supported by:
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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.

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

Abstract: 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.

Key words: multiobjective optimization problem, genetic algorithm, multiobjective optimization algorithm, Species mechanism, Tchebycheff approach

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