层次学习骨干粒子群优化算法

对骨干粒子群优化(BPSO) 种群多样性迅速丧失的原因进行分析, 提出层次学习骨干粒子群优化算法以克服早熟现象. 该算法中粒子依不同的学习概率向粒子自身的最优粒子、优胜粒子和群体最优粒子学习, 该机制使群体实现不同层次的搜索并有效维持群体的多样性. 此外, 群体最优粒子依概率采用跳跃策略以增强逃逸能力或采用扰动策略以提高解的质量. 将所提出的算法与多种改进的粒子群优化算法进行对比, 仿真结果表明, 所提出算法的综合表现优于其他算法.

Hierarchical learning bare-bones particle swarm optimization algorithm

The reasons for the fast loss of population diversity in bare-bones particle swarm optimization(BPSO) areanalyzed, and a hierarchical learning BPSO(HLBPSO) is developed to avoid the premature convergence. In the HLBPSO,each particle can learn from the personal best(pbest) particle, the superior particle and the global best(gbest) particleaccording to the learning probability. The learning mechanisms can provide hierarchical searching for maintaining the swarmdiversity. Moreover, the gbest particle adopts the jump strategy to strengthen the escape ability and the disturbance strategyto improve the quality of the solutions. The experimental results show that the proposed method significantly outperformsthe state-of-the-art PSO algorithms in terms of convergence speed and solution accuracy.