一种基于模拟退火的多目标Memetic算法

为了改善多目标进化算法的搜索效率，提出了基于模拟退火的多目标Memetic算法．此算法根据Pareto占优关系评价个体适应值，采用模拟退火进行局部搜索，并结合交叉算子和基于网格密度的选择机制改善算法的收敛速度和解的均衡分布．flowshop调度问题算例的仿真结果表明，基于模拟退火的多目标Memetic算法能够产生更接近Pareto前沿的近似集．

A Simulated-Annealing-Based Multi-Objective Memetic Algorithm

In order to improve the search efficiency of multi-objective evolutionary algorithms, a multi-objective Memetie algorithm based on simulated annealing is proposed. The method evaluates the individual fitness based on Pareto dominance relationship, applies simulated annealing to local search, and uses the crossover operator and a grid-density-based selection scheme to improve the convergence of the algorithm and to enhance the uniform distribu- tion of solutions. Simulations on multi-objective flowshop scheduling problems show that the proposed algorithm can generate approximation sets closer to the Pareto front of the problem.