基于人工蜂群的连续域蚁群优化算法

摘要/Abstract

摘要：

连续域蚁群优化算法是蚁群优化算法的一个重要研究方向，针对连续域蚁群优化算法(ACOR)计算时间较长、易陷入局部最优的问题，提出了一种基于人工蜂群的连续域蚁群优化算法(ABCACOR)。首先，引入一种替代机制来选择指导解，以替换原来的基于排序的选择方式，目的是节约计算时间和尽可能地保持搜索的多样性；其次，结合人工蜂群算法的搜索策略来提高算法的全局搜索能力，进一步减少计算时间和提高求解精度。通过对大量的测试函数进行仿真实验，结果表明，ABCACOR算法较现有的一些连续域蚁群算法具有更好的寻优能力。

关键词: 蚁群优化算法, 连续域, 人工蜂群, 全局搜索, 替代机制

Abstract:

Continuous ant colony optimization is an important research direction of ant colony optimization algorithms. The ant colony optimization for continuous domains (ACOR) requires long computation time and is easily trapped into local optimal solutions, so we propose artificial bee colony based ant colony optimization for continuous domain algorithm (ABCACOR) to solve the problems. Firstly, an alternative mechanism instead of the original sortbased selection method is introduced to guide solution choice, which saves computation time and secures the diversity as long as possible. Secondly, the artificial bee colony search strategy is adopted to improve the global search ability of the algorithm, thus the computation time is further reduced and the accuracy of solutions is improved. We evaluate the ABCACOR on a large number of test functions, and experimental results show that the ABCACOR outperforms some existing continuous ant colony optimization algorithms.

Key words: ant colony optimization algorithm;continuous domain;artificial bee colony;global search;alternative mechanism

周袅1,2，葛洪伟1,2，袁运浩2,苏树智2 . 基于人工蜂群的连续域蚁群优化算法[J]. J4, 2016, 38(06): 1156-1163.

ZHOU Niao1,2,GE Hongwei1,2,YUAN Yunhao2,SU Shuzhi2. Artificial bee colony based ant colony
optimization for continuous domains [J]. J4, 2016, 38(06): 1156-1163.

参考文献［9］

［1］	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.
［2］	Dorigo M,Gambardella L.Ant colony system: A cooperative learning approach to the travelling salesman problem［J］.IEEE Transactions on Evolutionary Computation,1997,1(1):
	Table 5the average evaluations and standard deviation of four algorithms on the benchmark functions表5四种算法对测试函数的平均最优值及标准差FOptimumAPIMeanStd.devCACOMeanStd.devCOACMeanStd.devABCACORMeanStd.devf101.04e-35.59e-48.77e-354.09e-340000f206.35e-31.70e-33.85e-212.36e-200000f302.43e-31.34e-36.66e-231.49e-220000f404.91e-12.547.35e-11.971.98e-15.44e-13.11e-25.27e-2f5000000000f605.18e-145.40e-142.89e-751.19e-740000f7-1675.93-1427.21119.63-1675.934.34e-12-1675.934.32e-12-1675.930f802.49e-41.39e-49.95e-39.95e-21.99e-21.30e-100f901.44e-24.61e-36.95e-166.09e-165.89e-1608.88e-160f1001.27e-47.42e-51.18e-311.32e-461.18e-311.32e-461.06e-301.75e-46f1102.67e-41.58e-41.35e-322.48e-471.35e-322.48e-471.34e-322.73e-48f123.075e-44.09e-37.67e-34.49e-41.16e-43.09e-48.73e-63.33e-43.36e-5f13-10.1532-8.73652.2828-7.83333.4229-10.15321.86e-14-10.15321.12e-15f14-10.4029-9.34752.1210-9.44782.4887-10.40291.52e-14-10.40292.78e-15f15-10.5364*-9.41042.1944-10.24911.4180-10.53641.43e-14-10.53641.59e-14f1602.61e-41.24e-41.088.50e-12.06e-14.27e-15.75e-52.12e-4f1701.41e-24.01e-36.60e-165.00e-165.89e-1608.88e-1605366.
［3］	Qu Wentai,Ding Wei.An improved ant colony algorithm and application in the TSP［J］.Systems Engineering—Theory & Practice,2006,26(5):9398.(in Chinese)
［4］	He Xiaofeng, Ma Liang. Quantuminspired ant colony algorithm for vehicle routing problem with time windows［J］.Systems Engineering—Theory & Practice,2013,33(5):12551261.(in Chinese)
［5］	Shou Yongyi,FU Ao.Multicolony ant algorithm for multiobjective resource constrained project scheduling［J］.Journal of Zhejiang University(Engineering Science),2010,44(1):5155.(in Chinese)
［6］	Duan Haibin,Ma Guanjun,Wang Daobo,et al.Improved ant colony algorithm for solving continuous space optimization problems［J］.Journal of System Simulation,2007,19(5):974977.(in Chinese)
［7］	Liang Ximing,Li Zhaohui,Long Wen,et al.Continuous domains ant colony algorithm with dimension mutation operator［J］.Journal of Computer Applications,2010,30(12):32043206.(in Chinese)
［8］	Jin Hao,Liu Weining.Ant colony algorithm for continuous function based on foraginghoming mechanism［J］.Computer Engineering and Applications,2012,48(1):2426.(in Chinese)
［9］	Huang Min, Jin Ting,Zhong Sheng,et al.Ant colony algorithm for solving continuous function optimization problem based on pheromone distributive function［J］.Journal of Guangxi Normal University(Natural Science Edition),2013,31(2):3438.(in Chinese)
［10］	Bilchev G,Parmee I C.The ant colony metaphor for searching continuous design spaces［C］∥LNCS: Lecture Notes in Computer Science,1995,993: 2539.
［11］	Monmarché N,Venturini G,Slimane M.On how pachycondyla apicalis ants suggest a new search algorithm［J］.Future Generation Computer Systems,2000,16(8):937946.
［12］	Dréo J,Siarry P.A new ant colony algorithm using the heterarchical concept aimed at optimization of multiminima continuous functions［C］∥Proc of the 3rd International Workshop on Ant Algorithms, 2002:216221.
［13］	Hu X M,Zhang J,Li Y.Orthogonal methods based ant colony search for solving continuous optimization problems［J］.Journal of Computer Science and Technology,2008,23(1),218.
［14］	Pourtakdoust S H,Nobahari H.An extension of ant colony system to continuous optimization problems［C］∥Proc of the 4th International Workshop on Ant Algorithms,2004:294301.
［15］	Socha K,Dorigo M.Ant colony optimization for continuous domains［J］.European Journal of Operational Research,2008,185(3):11551173.
［16］	Franca F,Coelho G P,Von Zuben F J,et al.Multivariate ant colony optimization in continuous search spaces［C］∥Proc of the 10th Annual Conference on Genetic and Evolutionary, GECCO’08,2008:916.
［17］	Leguizamon G, Coello C. An alternative ACOR algorithm for continuous optimization problems［C］∥Proc of the 7th International Conference on Swarm Intelligence,2010:4859.
［18］	Xiao Jing, Li Liangping.A hybrid ant colony optimization for continuous domains［J］.Expert Systems with Applications,2011,38(1): 1107211077.
［19］	Karaboga D,Akay B.A comparative study of artificial bee colony algorithm［J］.Applied Mathematics and Computation,2009,214(1): 108132.
［20］	Imanian N,Shiri M E,Moradi P.Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems［J］.Engineering Applications of Artificial Intelligence,2014,36(1): 148163.
［21］	Gao W,Liu S.A modified artificial bee colony algorithm［J］.Computers & Operactions Research,2012,39(1): 687697.
［22］	Li G,Niu P,Xiao X.Development and investigation of efficient artificial bee colony algorithm for numerical function optimization［J］.Applied Soft Computing,2012,12(1):320332.
	附中文参考文献：
［3］	屈稳太,丁伟.一种改进的蚁群算法及其在TSP中的应用［J］.系统工程理论与实践,2006,26(5):9398.
［4］	何小锋,马良.带时间窗车辆路径问题的量子蚁群算法［J］.系统工程理论与实践,2013,33(5): 12551261.
［5］	寿涌毅,傅奥.多目标资源受限项目调度的多种群蚁群算法［J］.浙江大学学报（工学版）,2010,44(1):5155.
［6］	段海滨,马冠军,王道波,等.一种求解连续空间优化问题的改进蚁群算法［J］.系统仿真学报,2007,19(5):974977.
［7］	梁昔明,李朝辉,龙文,等.含维变异算子的连续域蚁群算法［J］.计算机应用,2010,30(12):32043206.
［8］	金浩,刘维宁.基于觅食返巢机制连续域蚁群算法［J］.计算机工程与应用,2012,48(1):2426.
［9］	黄敏,靳婷,钟声,等.基于改进蚁群算法求解连续空间寻优问题［J］.广西师范大学学报（自然科学版）,2013,31(2): 3438.

[1]	庄鹤林, 杨火根, 夏小云, 廖伟志. 关于矩阵乘法问题的人工蜂群优化算法研究[J]. 计算机工程与科学, 2021, 43(12): 2131-2138.
[2]	包敏泽, 胡秀婷, 谢玉莹, 蒋波. 基于人工蜂群算法的p-center问题求解算法[J]. 计算机工程与科学, 2020, 42(06): 1127-1133.
[3]	赵凤1,2，孔令润1,2，马改妮1,2. 多目标粒子群和人工蜂群混合优化的阈值图像分割算法[J]. 计算机工程与科学, 2020, 42(02): 281-290.
[4]	常小刚1，赵红星2. 动态调节因子的邻域搜索人工蜂群算法[J]. 计算机工程与科学, 2019, 41(04): 673-681.
[5]	高雷阜,王飞. 基于混沌更新策略的蜂群算法在SVM参数优化中的应用[J]. 计算机工程与科学, 2017, 39(01): 199-205.
[6]	江涛1，袁景凌1，陈旻骋1，宋华明2. 基于加速收敛蜂群算法的资源感知调度器[J]. 计算机工程与科学, 2016, 38(08): 1595-1601.
[7]	徐曼舒，汪继文，邱剑锋，王心灵. 基于改进人工蜂群的模糊C-均值聚类算法[J]. J4, 2016, 38(06): 1238-1243.
[8]	黄文明，徐双双，邓珍荣，雷茜茜. 改进人工蜂群算法优化RBF神经网络的短时交通流预测[J]. J4, 2016, 38(04): 713-719.
[9]	臧培荃，孙晨骜，顾晓峰，吴滨，周长喜. 具有自适应趋向性和引导因子的人工蜂群算法[J]. J4, 2015, 37(09): 1692-1697.
[10]	郑晓菁. 基于人工蜂群优化法的多区域电力系统经济调度[J]. J4, 2015, 37(08): 1533-1539.
[11]	王志刚. 基于动态搜索策略的人工蜂群算法[J]. J4, 2015, 37(04): 734-739.
[12]	刘婷1，张立毅1,2，张晋斌3. 基于分布估计蜂群算法的多用户检测器[J]. J4, 2014, 36(07): 1316-1323.
[13]	王志刚，夏慧明. 求解车辆路径问题的人工蜂群算法[J]. J4, 2014, 36(06): 1088-1094.
[14]	何鹏飞1,周宗潭2，胡德文2. 基于蚁群优化算法的碎纸拼接[J]. J4, 2011, 33(7): 67-73.
[15]	童科,毛力. 基于蚁群优化算法求解矩形件排样问题[J]. J4, 2011, 33(7): 158-162.