一种改进的自适应微粒群优化算法

A modified self-adaptive particle swarm optimization

为了提高微粒群算法(PSO)优化高维目标的性能,提出了个体惯性权重自适应调整微粒群算法(PSO-IIW).PSO-IIW中微粒拥有个体的惯性权重以满足不同微粒对全局和局部搜索能力的不同需求,此权重在对微粒每次进化后的适应值进行评价的基础上被自适应地调整,以加快其收敛速度并逃离局部最优.用该方法与其他两种不同微粒群优化算法对3个经典函数在80,120和160维数进行仿真的结果进行比较,证明在解决高维度目标时可以有效提高微粒群算法的性能.

To enhance the performance of the particle swarm optimization （PSO）, the self-adaptive individual inertia weight adjustment particle swarm optimization （PSO-IIW） is proposed. Instead of holding the uniform inertia weight in the traditional PSO, each particle has an individual inertia weight in PSO-IIW, which can provide the different global and local searching performances for particles. The inertia weights will be adjusted self adaptively by evaluating the fitness value of the passed evolu tions to speed up convergence and escape local optima. This algorithm is applied to the three classical test functions of 80,120 and 160 dimensions and simulation results show that a marked improvement in performance over the traditional PSO.