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.吉林大学 计算机科学与技术学院,吉林 长春 130012;2.符号计算与知识工程教育部重点实验室,吉林 长春 130012
网络出版日期:2015-05,
纸质出版日期:2015-05-25
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张永刚, 张思博, 薛秋实. 求解约束满足问题的改进蚁群优化算法[J]. 通信学报, 2015,36(5):40-46.
HANGYong-gang Z, HANGSi-bo Z, UEQiu-shi X. Improved ant colony optimization algorithm for solving constraint satisfaction problem[J]. Journal on communications, 2015, 36(5): 40-46.
张永刚, 张思博, 薛秋实. 求解约束满足问题的改进蚁群优化算法[J]. 通信学报, 2015,36(5):40-46. DOI: 10.11959/j.issn.1000-436x.2015123.
HANGYong-gang Z, HANGSi-bo Z, UEQiu-shi X. Improved ant colony optimization algorithm for solving constraint satisfaction problem[J]. Journal on communications, 2015, 36(5): 40-46. DOI: 10.11959/j.issn.1000-436x.2015123.
为了克服传统的回溯算法在求解大型的约束满足问题时效率低,难以在合理的时间内求解这一问题。提出了基于启发式搜索的不完备性算法。结合不同算法特性,主要在蚁群优化元启发式约束求解算法的基础上提出了改进:一是在搜索之前用弧相容检查进行预处理以压缩搜索空间,二是提出了一种新的蚁群算法参数设置方案,提高算法的适应性。最后将改进后的算法应用于求解随机问题和组合优化问题。实验结果表明,改进后的算法求解效率得到大幅度提高。
The traditional backtracking algorithm was less efficient on solving large-scale constraint satisfaction problem
and more difficult to be solved within a reasonable time.In order to overcome this problem
many incompleteness algo-rithms based on heuristic search have been proposed.Two improvements based on ant colony optimization meta-heuristic constraint solving algorithm were presented:First
arc consistency checks was done to preprocess before exploring the search space
Second
a new parameter setting scheme was proposed for ant colony optimization to improve the effi-ciency of the search.Finally
the improved algorithm is applied to solve random problems and combinatorial optimization problems.The results of the experiment have showed its superiority.
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