• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (9): 137-143.doi: 10.3901/JME.2015.09.137

• 数字化设计与制造 • 上一篇    下一篇

基于过滤器技术的约束粒子群优化算法

王祝1, 刘莉1, 2, 龙腾1, 2, 寇家勋1   

  1. 1.北京理工大学宇航学院;2.北京理工大学飞行器动力学与控制教育部重点实验室
  • 出版日期:2015-05-05 发布日期:2015-05-05
  • 基金资助:
    国家自然科学基金(11372036,51105040)和航空科学基金 (2011ZA72003)资助项目

Constrained Particle Swarm Optimization Using the Filter Approach

WANG Zhu1, LIU Li1, 2, LONG Teng1, 2, KOU Jiaxun1   

  1. 1.School of Aerospace Engineering, Beijing Institute of Technology; 2.Key Laboratory of Dynamics and Control of Flight Vehicle of Ministry of Education, Beijing Institute of Technology
  • Online:2015-05-05 Published:2015-05-05

摘要: 工程设计中处理约束优化常采用罚函数法,但其优化结果敏感于惩罚因子,针对特定的实际问题往往需要多次试验以得到合适的罚因子取值。为了避免反复的参数选取测试过程,将过滤器约束处理机制和粒子群优化(Particle swarm optimization, PSO)相结合用于求解约束优化问题。过滤器方法基于多目标规划中的支配思想,以一组互不支配点所对应的目标值与违背度对构成过滤器,利用其处理约束可以避免使用罚函数。基于过滤器的约束PSO算法在粒子进化过程中,对各粒子历史最优解和粒子群历史最优解分别构造滤器,并依据可行性优先的粒子比较准则从对应的过滤器中选择最优解从而实现粒子的更新。然后,利用工程优化设计标准算例和翼型优化设计实例,将过滤器PSO算法和罚函数PSO算法、遗传算法进行比较研究,结果表明过滤器PSO算法能够获得较好的约束优化设计结果,是求解约束优化问题的一种有效方法。

关键词: 过滤器, 粒子群优化, 全局优化, 约束优化

Abstract: Penalty function approach is the common method for constrained optimization in engineering design. However, its results are sensitive to the penalty factor, and it is necessary to acquire the suitable penalty factor for specifically practical problems with many trials. To avoid the repeated parameter selection process, the filter constraint-handling mechanism and particle swarm optimization(PSO) are integrated for solving constrained optimization. The filter approach is based on the concept of domination from multi-objective optimization and the filter is constructed by a list of objective value and constraint violation pairs in which no pair is dominated by any other. Consequently, the constraint could be considered without using penalty function. In the evolution process of the constrained PSO using filter, the filters are constructed separately for historical optimum of each particle and historical optimum of the whole swarm, and the particle is updated using the optimum chosen from the corresponding filter with the feasible solution preferred comparison strategy. At last, the performance of the filter PSO algorithm is compared with the penalty PSO and genetic algorithm on some engineering design benchmarks. The simulation results show the filter PSO could find the more feasible, optimal and robust solution, and it is a effective method for constrained optimization.

Key words: constrained optimization, filter, global optimization, particle swarm optimization

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