Abstract：A concept of extensive-domain search is proposed to make ant colony optimization for continuous function overcome the sensitivity to initial domains of independent variables. By adding new elements to the gridding method, such as self-adaptive domain adjustment, self-adaptive ant size, self-adaptive pheromone increment and self-adaptive domain division, extensive-domain-search ant colony optimization (EDS-ACO) is put forward. Thus, the optimal solution can be found by EDS-ACO through an extensive search in the whole range of real numbers. Experiments show that EDS-ACO has the robustness since it can obtain the correct results in the case of initial domains without the optimal solution. The variation of initial domains has a small influence on convergence speed and computational accuracy of EDS-ACO.
[1] Dorigo M, Gambardella L M. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Trans on Evolutionary Computation, 1997, 1(1): 53-56 [2] Hu L L, Ge H. Improved Ant Colony Algorithm for Solving TSP. Computer Systems & Applications, 2012, 21(5): 198-200, 208 (in Chinese) (胡粼粼,葛 红.改进蚁群算法求解TSP.计算机系统应用, 2012, 21(5): 198-200, 208) [3] Li X L, Du B, Xu R, et al. Batch Scheduling on Uniform Parallel Machines with Non-identical Job Sizes. Computer Integrated Manufacturing Systems, 2012, 18(1): 102-110 (in Chinese) (李小林,杜 冰,许 瑞,等.同类机环境下不同尺寸工件的分批调度问题.计算机集成制造系统, 2012, 18(1): 102-110) [4] Colorni A, Dorigo M, Maniezzo V. Ant Colony System for Job-Shop Scheduling. Belgian Journal of Operations Research, Statistics and Computer Science, 1994, 34(1): 39-53 [5] Maniezzo V. Exact and Approximate Non-deterministic Tree Search Procedures for Quadratic Assignment Problem. INFORMS Journal on Computing, 1999, 11(4): 358-369 [6] Reimann M, Doerner K, Hartl R F. D-Ants: Savings Based Ants Divide and Conquer the Vehicle Routing Problem. Computers & Operations Research, 2004, 31(4): 563-591 [7] Liu Z S, Shen J S, Guan W. Design and Realization of a Hybrid Ant Colony Algorithm for Vehicle Routing Problem. Journal of Ma-nagement Sciences in China, 2007, 10(3): 15-22 (in Chinese) (刘志硕,申金升,关 伟.车辆路径问题的混合蚁群算法设计与实现.管理科学学报, 2007, 10(3): 15-22) [8] Bilchev G, Parmee I C. The Ant Colony Metaphor for Searching Continuous Design Spaces // Proc of the AISB Workshop on Evolutionary Computation. Sheffield, UK, 1995: 25-39 [9] Socha K, Dorigo M. Ant Colony Optimization for Continuous Domains. European Journal of Operational Research, 2008, 185(3): 1155-1173 [10] Xiao J, Li L P. A Hybrid Ant Colony Optimization for Continuous Domains. Expert Systems with Applications: An International Journal, 2011, 38(9): 11072-11077 [11] Gao S, Zhong J, Mo S J. Research on Ant Colony Algorithm for Continuous Optimization Problem. Microcomputer Development, 2003, 13(1): 21-22, 69 (in Chinese) (高 尚,钟 娟,莫述军.连续优化问题的蚁群算法研究.微机发展, 2003, 13(1): 21-22, 69) [12] Duan H B, Ma G J, Wang D B, et al. Improved Ant Colony Algorithm for Solving Continuous Space Optimization Problems. Journal of System Simulation, 2007, 19(5): 974-977 (in Chinese) (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.系统仿真学报, 2007, 19(5): 974-977) [13] Chen L, Shen J, Qin L. A Method for Solving Optimization Pro-blem in Continuous Space Using Ant Colony Algorithm. Systems Engineering-Theory & Practice, 2003, 23(3): 48-53 (in Chinese) (陈 崚,沈 洁,秦 玲.蚁群算法进行连续参数优化的新途径.系统工程理论与实践, 2003, 23(3): 48-53) [14] Li P C, Song K P, Yang E L. Phase Encoded-Based Quantum Ant Optimization. Systems Engineering-Theory & Practice, 2011, 31(8): 1565-1570 (in Chinese) (李盼池,宋考平,杨二龙.基于相位编码的量子蚁群算法.系统工程理论与实践, 2011, 31(8): 1565-1570) [15] Wang H L, Huang Z X, Li Z Y. Improved Research on Ant Colony Optimization in Continuous Spaces. Computer Engineering and Science, 2010, 32(3): 76-77 (in Chinese) (王慧玲,黄挚雄,李志勇.连续区域改进蚁群算法的研究.计算机工程与科学, 2010, 32(3): 76-77) [16] Zhou J X, Yang W D, Li Q. Improved Ant Colony Algorithm and Simulation for Continuous Function Optimization. Journal of System Simulation, 2009, 21(6): 1685-1688 (in Chinese) (周建新,杨卫东,李 擎.求解连续函数优化问题的改进蚁群算法及仿真.系统仿真学报, 2009, 21(6): 1685-1688)
2014, Vol. 27 
