摘要
针对已有算法对高维多目标问题存在多样性差且计算复杂的缺陷,提出利用冲突信息分区的高维多目标并行进化的智能优化算法。利用目标间的冲突信息将目标空间划分为若干子区间,独立进化以降低求解问题的难度;在每个子区间中加入其他子区间的聚合信息,考虑全局信息避免局部收敛;再根据子区间中目标数目的不同,采用并行独立优化算法缩小搜索空间,避免削弱进化算子的作用,提高算法优化性能。选取3种经典算法与该算法作对比,验证其优劣性。对比结果表明:该算法在车辆路径中可减少污染,并能在满足客户要求的情况下实现企业成本最小化。
In view of the shortcomings of existing algorithms for high-dimensional multi-objective problems such as poor diversity and complex computation,an intelligent optimization algorithm based on conflict information partition and high-dimensional multi-objective parallel evolution is proposed.The objective space is divided into several subintervals by using the conflict information among the objectives,and the independent evolution is used to reduce the difficulty of solving the problem;the aggregation information of other subintervals is added to each subinterval,and the global information is considered to avoid local convergence;then according to the different number of objectives in the subinterval,the parallel independent optimization algorithm is used to reduce the search space,so as to avoid weakening the role of the evolution operator.The optimization performance of the algorithm is improved.Three classical algorithms are compared with this algorithm to verify its advantages and disadvantages.The comparison results show that the algorithm can reduce the pollution in the vehicle routing,and can realize the enterprise cost minimization while meeting the customer requirements.
作者
程翔
李海平
姜立伟
谢明化
Cheng Xiang;Li Haiping;Jiang Liwei;Xie Minghua(Hunan Huanan Optoelectronic(Group)Co.,Ltd.,Changde 415007,China)
出处
《兵工自动化》
2021年第11期66-71,77,共7页
Ordnance Industry Automation
关键词
车辆路径规划
高维多目标
冲突信息
聚合
并行
vehicle routing planning
high dimensional multi-objective
conflict information
aggregation
parallel
