摘要
为了解决多目标的优化问题,提出了变动临近区域遗传算法。算法在解决设施布置问题时,改进了质化研究和量化研究中的一些不足。仿真结果表明,与传统的NPGA、VEGA遗传算法相比较,该算法在最终解个数、算法的稳定性、染色体的均匀程度等评价指标上为最优。
In order to solve multi-objective optimization problems,this paper presented neighbor change genetic algorithm.The algorithm improves some deficiencies in the qualitative research and quantitative research while solving facility layout problems.Simulation results show that algorithm performance is optimal compared with traditional NPGA,VEGA genetic algorithms in the final number,method of solution on stability,chromosome uniformity.
出处
《科学技术与工程》
北大核心
2012年第17期4197-4200,共4页
Science Technology and Engineering
基金
中央高校基本科研业务费专项基金(CHD2010ZY012)资助
关键词
遗传算法
PARETO最优解集
设施布置
多目标优化问题
genetic algorithm Pareto optimal set facility layout multiple objective optimization question
