| 求解全局优化问题的三角进化算法 |
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| 引用本文： | 罗长童,于波.求解全局优化问题的三角进化算法[J].数学研究及应用,2009,29(2):237-246. |
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| 作者姓名： | 罗长童 于波 |
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| 作者单位： | 吉林大学数学研究所, 吉林 长春 130012; 吉林建筑工程学院, 吉林 长春 130021;;大连理工大学应用数学系, 辽宁 大连 116023 |
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| 基金项目： | 国家自然科学基金(No.10671029). |
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| 摘 要： | This paper presents a hybrid heuristic-triangle evolution （TE） for global optimization. It is a real coded evolutionary algorithm. As in differential evolution （DE）, TE targets each individual in current population and attempts to replace it by a new better individual. However, the way of generating new individuals is different. TE generates new individuals in a Nelder- Mead way, while the simplices used in TE is 1 or 2 dimensional. The proposed algorithm is very easy to use and efficient for global optimization problems with continuous variables. Moreover, it requires only one （explicit） control parameter. Numerical results show that the new algorithm is comparable with DE for low dimensional problems but it outperforms DE for high dimensional problems.
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| 关 键 词： | 全局优化 三角进化算法 微分进化 数学分析 |
| 收稿时间： | 2007/3/19 0:00:00 |
| 修稿时间： | 2007/3/24 0:00:00 |
Triangle Evolution--A Hybrid Heuristic for Global Optimization |
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| LUO Chang Tong and YU Bo.Triangle Evolution--A Hybrid Heuristic for Global Optimization[J].Journal of Mathematical Research with Applications,2009,29(2):237-246. |
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| Authors: | LUO Chang Tong and YU Bo |
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| Institution: | Institute of Mathematics, Jilin University, Jilin 130012, China; Jilin Institute of Architecture and Civil Engineering, Jilin 130021, China;Department of Applied Mathematics, Dalian University of Technology, Liaoning 116024, China |
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| Abstract: | This paper presents a hybrid heuristic--triangle evolution (TE) for global optimization. It is a real coded evolutionary algorithm. As in differential evolution (DE), TE targets each individual in current population and attempts to replace it by a new better individual. However, the way of generating new individuals is different. TE generates new individuals in a Nelder-Mead way, while the simplices used in TE is 1 or 2 dimensional. The proposed algorithm is very easy to use and efficient for global optimization problems with continuous variables. Moreover, it requires only one (explicit) control parameter. Numerical results show that the new algorithm is comparable with DE for low dimensional problems but it outperforms DE for high dimensional problems. |
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| Keywords: | global optimization evolutionary computation differential evolution simplex method |
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