| Multiobjective extremal optimization with applications to engineering design |
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| 作者姓名： | CHEN Min-rong LU Yong-zai YANG Gen-ke |
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| 作者单位： | CHEN Min-rong1，2，3，LU Yong-zai1，YANG Gen-ke1 (1Department of Automation，Shanghai Jiao Tong University，Shanghai 200240，China) (2College of Information Science and Technology，Jinan University，Guangzhou 510632，China) (3College of Automation Science and Engineering，South China University of Technology，Guangzhou 510641，China) |
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| 基金项目： | Project (No.60574063) the National Natural Science Foundation of China |
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| 摘 要： | In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the constrained multiobjective optimization problems (MOPs). The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.
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| 关 键 词： | 最佳化 人工智能 工程设计 极值曲线 |
| 收稿时间： | 2007-06-20 |
| 修稿时间： | 2007-10-08 |
Multiobjective extremal optimization with applications to engineering design |
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| CHEN Min-rong LU Yong-zai YANG Gen-ke.Multiobjective extremal optimization with applications to engineering design[J].Journal of Zhejiang University Science,2007,8(12):1905-1911. |
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| Authors: | Chen Min-rong Lu Yong-zai Yang Gen-ke |
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| Institution: | (1) Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China;(2) College of Information Science and Technology, Jinan University, Guangzhou, 510632, China;(3) College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China |
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| Abstract: | In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the constrained multiobjective optimization problems (MOPs). The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems. |
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| Keywords: | Multiobjective optimization Extremal optimization (EO) Engineering design |
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