摘要
在多种群并行遗传算法(Multi-population Parallel Genetic A lgorithm)的基础上,将复合形法引入遗传算法来反映决策者对各目标函数的偏好信息,提出了一种新的结合复合形法的混合多目标遗传算法。算法将群体划分为相等规模的子群体,每个子群体对应于相应的子目标函数,各子群体具有独自的适应度评价函数,杂交和变异跨子群体边界执行。将复合形法引入遗传算法,通过次重要目标函数对应的子群体向重要目标函数对应的子群体进行压缩和缩转操作,使得综合后的基因在杂交和变异操作中向着更利于重要目标函数的群体方向进化,从而使获得的Pareto最优解集偏好于重要目标函数。通过数值仿真计算,结合复合形法的混合多目标遗传算法得到了某种程度上较好的协调最优解,具有良好的性能,对于求解多目标优化问题是可行有效的。
A new hybrid multi-objective optimization technique based on complex shape method has been developed. In this approach, sub-populations of the next generation are reproduced from the current population according to each of the objectives. Using RPGA operators, the selection method is repeated for each individual objective to fill up a portion of the mating pool, then the mating pool is shuffled and the other operators（ crossover and mutation） are performed, and the new population is divided into sub-populations randomly. Combined with complex shape method, by the operation of reflection, constriction, constricting to the best points and turn operation, the satisfaction of declsion-maker is reflected. The synthesized gene helps to accelerate the optimal speed, and diversify the mating pool population. It is proved though some examples and numerical simulation that this method is feasible and superior.
出处
《计算机应用研究》
CSCD
北大核心
2006年第7期70-72,共3页
Application Research of Computers
基金
粤港关键领域重点突破项目(2004A10402003)
关键词
复合形法
多目标优化
遗传算法
Complex Shape Method
Multi-objective Optimization
Genetic Algorithm
