摘要
针对现有遗传算法求解装箱问题收敛速度慢的问题,提出了一种改进的遗传算法。通过在初始化种群中加入降序最佳适应算法生成个体、最优个体保存策略和对适应度尺度进行变换,对现有的遗传算法进行改进。为了验证算法的有效性,设计了仿真实验。实验结果表明,改进后的算法找到最优解的概率更大、求解速度更快。
Low convergent speed is the main puzzling problem in solving the bin-packing problem by genetic algorithm. This paper proposes an improved genetic algorithm which adopts fitness scaling, optimal individual reservation strategy and adds an individual generated by the best fit decreasing heuristic algorithm to initial population. A simulation experiment is designed to validate the effectivity of the improved genetic algorithm. The experiment result shows that the improved genetic algorithm has larger probability of getting the best solution and higher convergent speed.
出处
《控制工程》
CSCD
北大核心
2016年第3期327-331,共5页
Control Engineering of China
基金
全军军事类研究生资助课题(2011JY002-499)
关键词
装箱问题
遗传算法
降序最佳适应
组合优化
Bin-packing problem
genetic algorithm
best fit decreasing
combinatorial optimization
