基于二阶振荡及自然选择的随机权重混合粒子群算法

Random weighted hybrid particle swarm optimization algorithm based on second order oscillation and natural selection

针对粒子群算法"早熟收敛"的缺点,提出一种混合粒子群算法.该算法采用最大速度线性递减的方法平衡全局寻优能力与算法收敛精度的矛盾,并用随机权重平衡算法的全局和局部搜索能力.学习因子二阶振荡使种群在粒子数目不变的情况下维持多样性,是提高全局搜索能力的主要方法.自然选择原理使算法改善了因二阶振荡和随机权重的加入而造成收敛速度降低的情况.测试实验表明,所提出的算法能避免早熟问题,有效地提高寻优能力.

To overcome the disadvantage of particle swarm optimization（PSO） algorithm such as easily trapping into local optimal solution, this paper proposes a kind of hybrid particle swarm optimization algorithm. The maximum speed linear degressivemethod can effectively compromise between global searching capability and algorithm convergence pi^ecision. The random weight can effectively balance the global and local searching ability of the algorithm. Second-order oscillative learning factor can maintain the population diversity under the condition of invariable particle number. At the same time, natural selection principle can improve the convergence speed. The function test experimental results show that the proposed algorithm can avoid premature convergence problem, and effectively improve its optimization ability.