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| A self-adaptive linear evolutionary algorithm for solving constrained optimization problems |
| KezongTANG,JingyuYANG,ShangGAO,TingkaiSUN |
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| (School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China;School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China) |
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| 摘要: |
| In many real-world applications of evolutionary algorithms, the fitness of an individual requires a quantitative measure. This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual’s relative strengths and weaknesses. Based on this strategy, searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ‘good’ individuals of the performance for a multiobjective optimization application, regardless of original space complexity. This is considered as our main contribution. In addition, the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase, namely, crossover and mutation. Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions. |
| 关键词: Multiobjective optimization Evolutionary algorithms Pareto optimal solution Linear fitness function |
| DOI： |
| Received:December 20, 2008Revised:July 08, 2009 |
| 基金项目:This work was supported by the National Natural Science Foundation of China (No.60803049, 60472060). |
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| A self-adaptive linear evolutionary algorithm for solving constrained optimization problems |
| Kezong TANG,Jingyu YANG,Shang GAO,Tingkai SUN |
| (School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China;School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China) |
| Abstract: |
| In many real-world applications of evolutionary algorithms, the fitness of an individual requires a quantitative measure. This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual’s relative strengths and weaknesses. Based on this strategy, searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ‘good’ individuals of the performance for a multiobjective optimization application, regardless of original space complexity. This is considered as our main contribution. In addition, the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase, namely, crossover and mutation. Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions. |
| Key words: Multiobjective optimization Evolutionary algorithms Pareto optimal solution Linear fitness function |
