摘要
水波优化算法(Water Wave Optimization,WWO)是一种基于浅水波理论的新兴智能优化算法。在简化水波优化算法(Simplified Water Wave Optimization,SimWWO)的基础上,提出水波优化算法的一个改进版本。针对WWO算法在寻优过程中未能有效利用水波历史状态和经验的问题,提出一种自适应的参数调整策略:根据水波进化过程中的性能改善指标自适应调整算法的波长系数,提高搜索效率;同时,针对算法后期容易陷入局部最优的情况,加入模拟退火的思想,以一定的概率接受劣质解,避免算法陷入局部最优。通过以上两个操作可以更好地平衡全局搜索和局部搜索。在CEC 2015函数测试集上进行比较,结果证明改进后的算法有效地提高了综合性能。
Water wave optimization（WWO）is a novel evolutionary algorithm inspired by the shallow wave theory.In this paper,we developed a modified version of simplified water wave optimization algorithm（SimWWO）.To fully utilize the history information and experience of the waves,we proposed an adaptive parameter adjustment strategy.The performance of waves on the evolutionary process is used as a feedback to adjust the wave length coefficient adaptively to improve search efficiency.Meanwhile,to avoid the problem of easily being lost in local optimum,the thought of simulated annealing is adopted to accept inferior solution with a certain probability.Through the above two operations,the algorithm achieves better balance between global search and local search.Computational experiments on the CEC 2015single-objective optimization test problems show that the modified algorithm effectively improves the overall performance.
出处
《计算机科学》
CSCD
北大核心
2017年第10期216-221,227,共7页
Computer Science
基金
"十二五"国家科技支撑计划基金项目(2012BAD10B01)
国家自然科学基金项目(61379123)资助
关键词
进化算法
水波优化
自适应参数
模拟退火
Evolut ionary algorithms, Water wave optimization, Adaptive parameter,Simulated annealing
