A Hybrid Adaptive Multi-objective Memetic Algorithm
WU Zhi-ming · Kongzhi yu juece · 2006
Memetic algorithm is one of the most efficient methods for multi-objective optimization problems,incorporating local search into evolutionary computation and having high global search ability.Hybrid adaptive memetic algorithm(HAMA) uses a simulated annealing-based weighted-sum method to perform local search,uses Pareto-based approach to implement crossover and mutation,and employs perturbation to enhance the exploration capability of the algorithm.The evolution is made self-adjusted according to optimization ratio for better efficiency and robustness of the algorithm.A testing example shows that HAMA can generate near-Pareto optimal and well-extended approximation set.