多种群退火贪婪混合遗传算法

遗传算法是应用比较广泛的一种随机优化算法,遗传算法的收敛速度与问题解的质量是影响算法寻优性能的一对主要矛盾。为了提高遗传算法的性能,论文通过将局部搜索能力较强的贪婪算法引入遗传算法,并且同模拟退火和多种群并行遗传进化思想有机结合起来的方法,提出了一个改进型的算法——多种群退火贪婪混合遗传算法(MultigroupAnnealingGreedyHybridGeneticAlgorithm,简称MAGHGA)。仿真结果表明,该算法避免了在遗传算法中存在的早熟收敛问题,增强了算法的全局收敛性,同时也有效地提高了算法的收敛速度。

Multigroup Annealing Greedy Hybrid Genetic Algorithm

Genetic algorithm is a widely used stochastic optimization method.The convergence speed of genetic algorithm and the quality of problem result are the main inconsistency which affects performance of GA.To improve the performance of genetic algorithm,greedy algorithm which has more local search ability is imported to genetic algorithm and is well combined with the ideas of simulated annealing and multigroup parallel evolution in this paper.A new algorithm is derived,that is the multigroup annealing greedy hybrid genetic algorithm.Simulation results show that this method not only avoids the premature convergence problem existed in genetic algorithms,but also enhances the globe convergence,thus availably improves the convergence velocity.