| AN IMPROVED ANT COLONY ALGORITHM IN CONTINUOUS OPTIMIZATION |
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| 作者姓名： | Ling CHEN Jie SHEN Ling QIN Hongjian CHEN |
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| 作者单位： | Ling CHEN Jie SHEN Ling QIN Hongjian CHEN
Department of Computer Science&Engeering
Yangzhou University，Yangzhou 225009，China
National Key Lab of Novel Software Tech.
Nanjing Univ，Nanjing 210093，China |
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| 基金项目： | This research was supported in part Chinese National Science Foundation under contract 60074013,Chinese National Foundation of High Performance Computing under contract 00219 and Science Foundation of Jiangsu Educational Commission,China |
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| 摘 要： | A modified ant colony algorithm for solving optimization problem with continuous parameters ispresented. In the method, groups of candidate values of the components are constructed, and eachvalue in the group has its trail information. In each iteration of the ant colony algorithm, the methodfirst chooses initial values of the components using the trail information. Then GA operations ofcrossover and mutation can determine the values of the components in the solution. Our experimentalresults on the problem of nonlinear programming show that our method has a much higherconvergence speed and stability than those of simulated annealing(SA)and GA.
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| 关 键 词： | 连续最优化问题 蚁群算法 遗传算法 模拟退火算法 非线性规划 |
An improved ant colony algorithm in continuous optimization |
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| Ling CHEN Jie SHEN Ling QIN Hongjian CHEN.AN IMPROVED ANT COLONY ALGORITHM IN CONTINUOUS OPTIMIZATION[J].Journal of Systems Science and Systems Engineering,2003,12(2):224-235. |
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| Authors: | Ling Chen Jie Shen Ling Qin Hongjian Chen |
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| Institution: | 1. Department of Computer Science&Engeering Yangzhou University, Yangzhou 225009, China;National Key Lab of Novel Software Tech. Nanjing Univ, Nanjing 210093, China lchen@yzcn.net
2. Department of Computer Science&Engeering Yangzhou University, Yangzhou 225009, China |
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| Abstract: | A modified ant colony algorithm for solving optimization problem with continuous parameters is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then GA operations of crossover and mutation can determine the values of the components in the solution. Our experimental results on the problem of nonlinear programming show that our method has a much higher convergence speed and stability than those of simulated annealing (SA) and GA. |
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| Keywords: | Ant colony algorithm optimization nonlinear programming |
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