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针对现有Memetic算法收敛速度慢、容易陷入局部极值等不足,提出一种基于改进粒子群优化和模拟退火算法的Memetic算法(简称为PMemetic算法).在PMemetic算法,基于人工萤火虫算法邻域结构思想改进粒子群优化算法,并将其作为全局搜索策略;同时,采用模拟退火算法作为局部搜索策略.将PMemetic算法应用到6个典型的函数优化问题中,并与粒子群算法进行比较分析,实验结果表明PMemetic算法提高了全局搜索能力、收敛速度和解的精度.
Aiming at the shortcomings of the existing Memetic algorithm, such as slow convergence speed and easily falling into local extremum, a Memetic algorithm based on improved particle swarm optimization and simulated annealing algorithm (PMemetic algorithm for short) is proposed.In the PMemetic algorithm, based on the artificial firefly algorithm neighborhood structure The idea of improved particle swarm optimization algorithm, and as a global search strategy; the same time, simulated annealing algorithm as a local search strategy. PMemetic algorithm is applied to six typical function optimization problems, and particle swarm optimization algorithm for comparative analysis, experiments The results show that the PMemetic algorithm improves the global search ability, the convergence speed and the precision of solution.