摘要
针对惯性权重线性递减粒子群算法(LDWPSO)不能适应复杂的非线性优化搜索过程的问题,提出了一种动态改变惯性权重的自适应粒子群算法(DCWPSO),在该算法中引入聚焦距离变化率的概念,并根据它对粒子群算法搜索能力的影响,将惯性因子表示为关于聚焦距离变化率的函数。在每次迭代时算法可根据当前粒子群聚焦距离变化率的大小动态地改变惯性权重,从而使算法具有动态自适应性。对6个典型函数的测试结果表明,DCWPSO算法的收敛速度明显优于LDWPSO算法,收敛精度也有所提高。
A new adaptive Particle Swarm Optimization algorithm with dynamically changing inertia weight （DCWPSO） was presented to solve the problem that the linearly decreasing weight （LDWPSO） of the Particle Swarm Optimization algorithm cannot adapt to the complex and nonlinear optimization process. The rate of cluster focus distance changing was introduced in this new algorithm and the weight was formulated as a function of this factor according to its impact on the search performance of the swarm. In each iteration process, the weight was changed dynamically based on the current rate of cluster focus distance changing value,which provides the algorithm with effective dynamic adaptability. The algorithm of LDWPSO and DCWPSO were tested with six well-known benchmark functions. The experiments show that the convergence speed of ECWPSO is significantly superior to LDWOSI,and the convergence accuracy is increased.
出处
《计算机科学》
CSCD
北大核心
2009年第2期227-229,256,共4页
Computer Science
基金
国家科技支撑计划项目(2006BAF01A46)
上海市科技发展基金重点项目(061612058)
上海市基础研究重点项目(06JC14066)
上海市登山计划重点项目(061111006)资助
关键词
粒子群优化
惯性权重
聚焦距离变化率
自适应
Particle Swarm Optimization （PSO）, Inertia weight,Rate of cluster focus distance changing, Adaptability
