摘要
针对粒子群优化算法的"早熟"问题,提出了一种新型分阶段粒子群优化算法。该算法通过调整惯性权重和加速系数使粒子自组织地跟踪局部吸引域和全局吸引域来扩大粒子的搜索空间和提高粒子的收敛精度,同时根据粒子处于不同的阶段实施相应的变异策略来增加种群的多样性。通过经典函数的测试结果表明,新算法的全局搜索能力有了显著提高,并且能够有效避免早熟问题。
A novel multistage particle swarm optimization is developed for solving premature convergence of particle swarm optimization.The particles are organized to track the domain of attraction of local optimum for enlarging search space and the domain of attraction of global optimum for improving convergence performance by adaptively adjusting the acceleration coefficients and the inertia weight.Meanwhile the corresponding strategies with mutation are adopted in different stages of the new algorithm to further enhance diversity of population.Experimental results for complex function optimization show this algorithm improves the global convergence ability and efficiently prevents the algorithm from the local optimization and early maturation.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第24期81-82,138,共3页
Computer Engineering and Applications
关键词
粒子群优化算法
惯性权重
加速系数
Particle Swarm Optimization （PSO）
inertia weight
acceleration coefficients
