一种混合自适应多目标Memetic算法

Memetic算法是求解多目标优化问题最有效的方法之一，融合了局部搜索和进化计算，具有较高的全局搜索能力．混合自适应多目标Memetic算法（HAMA）用基于模拟退火的加权法进行局部搜索，采用Pareto法实现交叉和变异，通过扰动增强算法的exploration能力，且进化过程可根据改善率自适应调整，以提高搜索效率并改善算法的鲁棒性．算例测试说明HAMA能产生更接近Pareto前沿且多样性更好的近似集．

A Hybrid Adaptive Multi-objective Memetic Algorithm

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.