摘要
针对粒子群算法容易陷入局部最优的问题,加入变异机制,结合新的变异条件,将搜索域划分为有限子区域,依据子区域的历史访问量,确定变异粒子变异到该区域的概率,使得算法能更加有效地跳出局部最优,提出区域二分法提高搜索精度。实验结果表明,基于区域动态概率的变异机制以及区域二分法的粒子群算法能更加有效地跳出局部最优,得到更加精确的目标解。
According to the problem of easily falling into local optimum of particle swarm optimization algorithm,a variation mechanism with new mutation conditions was added.The search area was divided into limited sub-regions and in accordance with the historical region-visited times of each sub-region,the probabilities of the sub-regions that particle varied to were determined.Combining with the regional dichotomy,it becomes a method which jumps out of local optimal more effectively,and obtains more accurate targets.
出处
《计算机工程与设计》
北大核心
2016年第5期1362-1366,1374,共6页
Computer Engineering and Design
基金
国家自然科学基金项目(61173072)
国家自然科学青年基金项目(61003209)
江苏省自然科学基金项目(BK2011824)
江苏省高校自然科学研究基金项目(10KJB520012)
关键词
粒子群
变异
区域访问量
区域动态概率
区域二分法
particle swarm optimization
variation
region-visited times
regional dynamical probability
regional dichotomy