基于模拟退火的自适应粒子群优化算法的改进策略

针对PSO算法在求解问题的优化问题中易陷入局部收敛且收敛速度较慢等缺陷,引入一种初始化改进策略,并将模拟退火算法与PSO算法相结合,提出了一种全新的算法。该算法将寻优过程分为两个阶段:为了提高算法的执行速度,前期使用标准PSO算法进行寻优,后期运用模拟退火思想对PSO中的参数进行优化搜索最优解。最后将该算法应用于八个经典的单峰/多峰函数中。模拟结果表明,该算法有效地避免了早熟收敛现象,并提高了收敛速度,从而提高了PSO算法解决全局优化的性能。

Strategy of adaptive simulated annealing particle swarm optimization algorithm

In PSO algorithm, it tends to suffer from premature convergence and slow rate of convergence on solving the problem of optimization problems. This paper proposed a new algorithm about initialization and simulated annealing algorithm combined with the PSO for function optimization. It divided the new method into two phases. In order to improve the convergence rate, pre-standard optimization algorithm, and post ideas on the use of simulated annealing to optimize the parameters of PSO for searching the optimum. It applied eight classic unimodal/multimodal function. Compared with other algorithms, the simulation results show that the algorithm avoids the premature convergence phenomenon, enhanced the convergence rate and improves the performance of global optimization.