摘要
提出一种自适应反向竞争和声搜索算法.该算法简单分析和声搜索算法步长设置的盲目性,提出一种自适应步长调整操作.算法融合反向学习策略的优势,建立末位淘汰竞争选择机制,以进一步提高算法的全局搜索能力,防止算法陷入局部最优.为验证文中算法的有效性,优化经典测试函数,数值结果表明文中算法在精度和鲁棒性方面比和声搜索算法及目前较优的改进和声搜索算法更好.最后通过优化求解热交换器和减速器设计问题,证明文中算法求解结果优于其他算法.
A self-adapted harmony search algorithm with opposed competition （ SHSOC ） is proposed. The blindness of bandwidth setting of harmony search algorithm is analyzed. The adaptive bandwidth adjustment is employed. Meanwhile, the superiority of the opposed learning strategy is integrated into the proposed algorithm, and the competition selection mechanism of end elimination is established to further improve global search ability and keep the algorithm from falling into local optima. The proposed algorithm is tested on several classic functions to evaluate the performance. The numerical results show the superiority of SHSOC in accuracy and robustness compared with harmony search algorithm and some state-of-the-art harmony search variants. Moreover, SHSOC can solve the optimization problems of the heat exchanger and the speed reducer design, and the results show that SHSOC is better than any other algorithm.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第4期305-312,共8页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60674021)资助
关键词
自适应步长调整
反向学习
竞争选择
精度
Adaptive Bandwidth Adjustment
Opposed Learning
Competition Selection
Accuracy
