基于离散差分进化算法的随机车辆路径问题
发布时间：2018-07-15 09:35
【摘要】：针对差分进化算法求解组合优化问题存在的局限性,引入计算机语言中的2种按位运算符,对差分进化算法的变异算子进行重新设计,用来求解不确定需求和旅行时间下同时取货和送货的随机车辆路径问题(SVRPSPD)。通过对车辆路径问题的benchmark问题和SVRPSPD问题进行路径优化,并同差分进化算法和遗传算法的计算结果进行比较,验证了离散差分进化算法的性能。结果表明,离散差分进化算法在解决复杂的SVRPSPD问题时,具有较好的优化性能,不仅能得到更好的优化结果,而且具有更快的收敛速度。
[Abstract]:In view of the limitations of differential evolution algorithm for solving combinatorial optimization problems, 2 kinds of bit operators in computer language are introduced, and the mutation operators of differential evolution algorithm are redesigned to solve the random vehicle routing problem (SVRPSPD) for the simultaneous delivery and delivery of goods and goods under uncertain demand and travel time. The benchmark problem and the SVRPSPD problem are optimized and compared with the results of the differential evolution and genetic algorithms. The performance of the discrete differential evolution algorithm is verified. The results show that the discrete differential evolution algorithm has better optimization performance in solving complex SVRPSPD problems and not only can get better optimization results. And it has a faster rate of convergence.
【作者单位】： 天津师范大学管理学院;北京航空航天大学经济管理学院;
【基金】：国家自然科学基金资助项目(71071008) 天津市2012年度哲学社会科学研究规划项目(TJGL12-079)
【分类号】：U492.22;TP18
[Abstract]:In view of the limitations of differential evolution algorithm for solving combinatorial optimization problems, 2 kinds of bit operators in computer language are introduced, and the mutation operators of differential evolution algorithm are redesigned to solve the random vehicle routing problem (SVRPSPD) for the simultaneous delivery and delivery of goods and goods under uncertain demand and travel time. The benchmark problem and the SVRPSPD problem are optimized and compared with the results of the differential evolution and genetic algorithms. The performance of the discrete differential evolution algorithm is verified. The results show that the discrete differential evolution algorithm has better optimization performance in solving complex SVRPSPD problems and not only can get better optimization results. And it has a faster rate of convergence.
【作者单位】： 天津师范大学管理学院;北京航空航天大学经济管理学院;
【基金】：国家自然科学基金资助项目(71071008) 天津市2012年度哲学社会科学研究规划项目(TJGL12-079)
【分类号】：U492.22;TP18
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