基于Metropolis准则的自适应模拟退火粒子群优化

Adaptive Simulated Annealing Particle Swarm Optimization Algorithm Based on Metropolis

为有效提高粒子群优化(PSO)算法搜索最优解的效率,提出一种基于Metropolis准则的自适应模拟退火粒子群优化(ASAPSO)算法。首先,ASAPSO采用一种新的自适应极值惯性权重方式,能有效平衡粒子种群的全局搜索和局部搜索过程;其次,通过分析粒子个体间信息交流方式,构建粒子飞行学习交流的中心粒子,该粒子能有效增强粒子个体的社会学习能力;最后,基于Metropolis准则的模拟退火选择概率,粒子群体在中心粒子的引领下向着全局最优解不断逼近,能有效避免粒子群体陷入局部最优区域。仿真实验表明,相比其他测试算法,ASAPSO收敛精度高,在多种标准测试函数中收敛率(CR)可达94%以上,能有效提高粒子种群的寻优效率。

In order to effectively improve the efficiency of particle swarm optimization(PSO)algorithm in searching the optimal solution,an adaptive simulated annealing particle swarm optimization(ASAPSO)algorithm based on Metropolis criterion is proposed.Firstly,ASAPSO adopts a new adaptive extreme inertia weight method,which can effectively balance the global search and local search process of particle population.Secondly,by analyzing the information exchange mode between particle individuals,the central particle of particle flight learning exchange is constructed,which can effectively enhance the social learning ability of particle individuals.Finally,based on the simulated annealing selection probability of Metropolis criterion,the particle group is approaching the global optimal solution under the guidance of the central particle,which can effectively avoid the particle group falling into the local optimal region.The simulation results show that compared with other test algorithms,ASAPSO has obvious advantages in high convergence accuracy.In a variety of standard test functions,the convergence rate(CR)of ASAPSO can reach more than 94%,which can effectively improve the optimization efficiency of particle population.