A Self-Organizing Particle Swarm Optimization Algorithm and Application

Yuanxia Shen, Chuanhua Zeng
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引用次数: 5

Abstract

A self-organizing particle swarm optimization algorithm is developed for solving premature convergence of particle swarm optimization. According to adaptively adjusting acceleration coefficients and inertia weight, the particles are organized to track the domain of attraction of local optimum and the domain of attraction global optimum respectively during the search. Meanwhile the corresponding strategies with mutation are adopted in different stages of this algorithm to further enhance diversity of population. Experimental results for complex function optimization and nonlinear system identification show that this algorithm improves the global convergence ability and efficiently prevents the algorithm from the local optimization and early maturation.
一种自组织粒子群优化算法及其应用
针对粒子群优化算法的早熟收敛问题,提出了一种自组织粒子群优化算法。根据自适应调整的加速度系数和惯性权重,组织粒子在搜索过程中分别跟踪局部最优的引力域和全局最优的引力域。同时在算法的不同阶段采用相应的变异策略,进一步增强种群的多样性。复杂函数优化和非线性系统辨识的实验结果表明,该算法提高了全局收敛能力,有效地防止了算法的局部优化和早熟。
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