摘要
针对平衡运输问题,文中提出了一种基于蒙特卡洛相似度遗传算法的求解算法。首先,利用矩阵元素对种群个体进行初始化,增加了种群的多样性;其次,设计了动态变异率算子和随机变异策略,以增强算法的搜索能力,加快收敛速度;最后,采用蒙特卡洛相似度接收的方式,避免算法陷入局部最优解问题。通过收敛速度、最优解偏差率、相对标准差等参数对基本遗传算法GA和改进遗传算法IGA进行比较,验证了所提算法的有效性。针对杭州地理数据,设计开发了基于ArcGIS平台的运输配送系统,实现了平衡运输问题的求解功能,系统测试表明了所提算法的有效性。
Aiming at the problem of balanced transportation,this paper proposes a Monte Carlo similarity based genetic algorithm.Firstly,the matrix elements are used to initialize the population,which increases the diversity of the population.Secondly,the dynamic mutation rate operator and the random mutation strategy are designed to enhance the search ability of the algorithm and accelerate the convergence speed.Finally,Monte Carlo similarity is adopted to avoid falling into the local optimal solution problem.The effectiveness of the algorithm is verified by the comparison of the convergence rate,the optimal solution deviation rate and the relative standard deviation by the basic genetic algorithm GA and the improved genetic algorithm IGA.According to the geographic data of Hangzhou,the transportation and distribution system based on ArcGIS platform is designed and developed to realize the function of solving the balanced transportation problem.The test results show the effectiveness of the proposed algorithm.
作者
李远锋
李章维
秦子豪
胡俊
张贵军
LI Yuan-feng;LI Zhang-wei;QIN Zi-hao;HU Jun;ZHANG Gui-jun(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《计算机科学》
CSCD
北大核心
2020年第10期215-221,共7页
Computer Science
基金
国家自然科学基金(61573317)
浙江省教育厅一般科研项目(工程硕士专项)(Y201840644)。
关键词
运输问题
遗传算法
交叉变异
蒙特卡洛相似度
Transportation problem
Genetic algorithm
Cross variation
Monte Carlo similarity
