沈阳工业大学理学院
针对蚁群算法(ant colony algorithm,ACO)收敛速度慢和易陷入局部最优等问题,提出一种融合多策略改进的自适应蚁群算法(improved ant colony algorithm,IACO)。首先,通过透镜成像反向学习的拉丁超立方策略进行种群初始化,增加种群多样性的同时也提升初始解的质量;其次,通过动态信息素更新规则和自适应调整机制对信息素进行更新,进一步提高全局搜索能力;再次,采用t分布对信息素浓度进行变异扰动,可以增强其跳出局部最优能力;最后,将IACO与标准ACO及其他经典群智能优化算法在多个标准测试函数上作仿真对比,在Rastrigin、Sphere和Ackley 3个测试函数上分别与标准ACO、GA(genetic algorithm)和PSO(particle swarm optimization)进行对比,同时,在Rastrigin上分别控制蚂蚁数量和启发式信息权重与标准ACO进行对比,实验结果表明IACO具有更好的收敛性能和稳定性能。
338 | 0 | 63 |
下载次数 | 被引频次 | 阅读次数 |
[1]张宇轩.基于遗传算法的多属性群体决策问题研究[D].沈阳:沈阳工业大学,2021.ZHANG Y X. Research on multi-attribute group decision making problemsbased on genetic algorithm[D]. Shenyang:Shenyang University of Technology, 2021.(in Chinese)
[2]李娜娜.基于改进粒子群算法的多目标优化问题研究[D].贵阳:贵州民族大学,2022.
[3]陆观,刘宏林,徐一鸣.基于改进粒子群算法的光纤光栅压触觉传感阵列测点优化[J].南通大学学报(自然科学版),2021, 20(3):41-48.LU G, LIU H L, XU Y M. Optimization of fiber Bragg grating tactile sensor array based on improved particle swarm optimization algorithm[J]. Journal of Nantong University(Natural Science Edition), 2021, 20(3):41-48.(in Chinese)
[4] DORIGO M, MANIEZZO V, COLORNI A. Ant system:optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics Part B,Cybernetics, 1996, 26(1):29-41.
[5]周东健,张兴国,马海波,等.基于栅格地图-蚁群算法的机器人最优路径规划[J].南通大学学报(自然科学版),2013, 12(4):91-94.ZHOU D J, ZHANG X G, MA H B, et al. Optimal path planning for mobile robot based on grid map with ant colony algorithm[J]. Journal of Nantong University(Natural Science Edition), 2013, 12(4):91-94.(in Chinese)
[6]唐存花,汤可宗.求旅行商问题的幂律变换优化蚁群算法[J].软件导刊,2024, 23(2):74-83.TANG C H, TANG K Z. Power-law transformation optimized ant colony system for the travel quotient problem[J].Software Guide, 2024, 23(2):74-83.(in Chinese)
[7]宋佳容,申雪峰,冯悦,等.基于改进蚁群算法的绿色柔性流水车间调度问题研究[J].辽宁工业大学学报(自然科学版),2023, 43(4):245-251.SONG J R, SHEN X F, FENG Y, et al. Research on scheduling problems in green flexible flow shop based on improved ant colony algorithm[J]. Journal of Liaoning University of Technology(Natural Science Edition), 2023, 43(4):245-251.(in Chinese)
[8]高大利.自适应蚁群算法在路由协议中的应用研究[J].东莞理工学院学报,2022, 29(1):85-90.GAO D L. Application of adaptive ant colony algorithm in routing protocol[J]. Journal of Dongguan University of Technology, 2022, 29(1):85-90.(in Chinese)
[9] GOUDARZI F, ASGARI H, AL-RAWESHIDY H S. Traffic-aware VANET routing for city environments:a protocol based on ant colony optimization[J]. IEEE Systems Journal,2019, 13(1):571-581.
[10]刘新宇,谭力铭,杨春曦,等.未知环境下的蚁群-聚类自适应动态路径规划[J].计算机科学与探索,2019, 13(5):846-857.LIU X Y, TAN L M, YANG C X, et al. Self-adjustable dynamic path planning of unknown environment based on ant colony-clustering algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(5):846-857.(in Chinese)
[11]王梦绚,万仁霞,苗夺谦,等.基于三支决策的蚁群聚类算法[J].昆明理工大学学报(自然科学版),2024, 49(1):83-97.WANG M X, WAN R X, MIAO D Q, et al. An ant colony clustering algorithm based on three-way decision[J].Journal of Kunming University of Science and Technology(Natural Science), 2024, 49(1):83-97.(in Chinese)
[12]高茂源,王好臣.基于改进蚁群算法的移动机器人路径规划[J].传感器与微系统,2021, 40(6):142-144.GAO M Y, WANG H C. Path planning for mo bile robots based on improved ant colony algorithm[J]. Transducer and Microsystem Technologies, 2021, 40(6):142-144.(in Chinese)
[13] MCKAY M D, BECKMAN R J, CONOVER W J. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J].Technometrics, 1979, 21(2):239-245.
[14] WU D, RAO H H, WEN C S, et al. Modified sand cat swarm optimization algorithm for solving constrained engineering optimization problems[J]. Mathematics, 2022, 10(22):4350.
[15]钟发海.基于t分布的力导向图布局及其近似求解方法[D].济南:山东大学,2023.ZHONG F H. Force-directed graph layout based on the tdistribution and its approximate solution method[D]. Jinan:Shandong University, 2023.(in Chinese)
[16]吕苏琪.基于独立t分布的稳健因子分析[D].昆明:云南财经大学,2023.LüS Q. Robust factor analysis based on independent t distribution[D]. Kunming:Yunnan University of Finance and Economics, 2023.(in Chinese)
[17]徐鹏.偏正态分布与偏t分布的研究[D].南京:南京邮电大学,2022.XU P. Research on skew normal distribution and skew-t distrib[D]. Nanjiang:Nanjing University of Posts and Telecommunications, 2022.(in Chinese)
[18] YAO X, LIU Y, LIN G M. Evolutionary programming made faster[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(2):82-102.
基本信息:
DOI:10.12194/j.ntu.20240508001
中图分类号:TP18
引用信息:
[1]刘禹彤,李媛,郑新宇.融合多策略改进的自适应蚁群算法[J].南通大学学报(自然科学版),2024,23(04):36-44.DOI:10.12194/j.ntu.20240508001.
基金信息:
辽宁省兴辽英才计划项目(XLYC2008005)