摘要
很多现实的优化问题往往是动态和多峰的,这就需要优化算法既能够发现尽可能多的最优解,同时还要追踪到这些最优解在动态环境中的变化轨迹.为了解决这种动态多峰优化问题,本文提出了一种Memetic粒子群优化算法.在提出的算法中,利用一种新的species构造方法来保证其能够发现不同最优解所在搜索区域,利用一种适应性的局域搜索算子来增强species追踪到最优解的能力,利用重新初始化策略来进一步改善算法在动态多峰环境中的性能.通过对一组标准动态测试函数——移动峰问题的仿真实验来检验所提出的MPSO算法在求解动态多峰优化问题的有效性.
Many real-world optimization problems are both dynamic and multi-modal, which require an optimization algorithm not only to find as many as possible optima under a specific environment but also to track their trajectory over dynamic environments. To address this requirement, this paper investigates a memetic particle swarm algorithm for dynamic multi-modal optimization problems. Within the framework of the proposed algorithm, a new speciation method is employed to locate multiple peaks and an adaptive local search method is also hybridized to accelerate the exploitation of species generated by the speciation method. In addition, the re-initialization schemes are introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the memetic particle swarm algorithm in comparison with several statewof-the-art algorithms in the literature. The experimental results show the efficiency of our proposed algorithm for dynamic multi-modal optimization problems.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2013年第6期1577-1586,共10页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(70931001
71001018
71071028)
中央高校基本科研业务费专项资金(N110404019
N110204005)
中国博士后科学基金(2012T50266)
