摘要
提出一种结合收缩与发散操作的自适应粒子群算法,其在运行过程中通过判断种群收敛情况与进化情况,自适应地选择粒子的运动行为。通过收缩操作使群体向极值点快速靠拢,通过发散操作保证粒子有能力跳出局部极值点,并根据种群进化状况在两种操作间转换。实验表明,该算法具有较强的跳出局部极值、逼近全局最优解的能力,在高维多峰函数上有较出色的表现。
An adaptive particle swarm optimization algorithm with shrink and expansion operation was presneted,which can adaptively choose the behavior of the particle by detecting the degree of convergence during running.These two operations make particle swarm converge to extreme point and jump out of it quickly,and the evolutionary status of the population make them convert between these two operations adaptively.Experimental data show this algorithm has strong ability to get rid of the local optima and approach to global optima,especially in tackling the problem of high dimension multimodal function.
出处
《计算机科学》
CSCD
北大核心
2015年第S1期48-51,共4页
Computer Science
基金
国家自然科学基金项目(61363067)资助
关键词
粒子群优化算法
收缩操作
发散操作
自适应检测
Particle swarm optimization,Shrink operation,Expansion operation,Adaptive detection
