A new multiobjective evolutionary algorithm |
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| Affiliation: | 1. Central Aerohydrodynamic Institute, Zhukovskii, Russia;2. V. A. Steklov Mathematical Institute of the Russian Academy of Sciences, Moscow, Russia;3. Moscow Institute of Physics and Technology, Dolgoprudnyi, Russia;1. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong;2. Department of Electrical and Computer Engineering, University of Illinois at Urbana–Champaign, Urbana, IL 61801, USA;3. Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200433, China |
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| Abstract: | The Pareto-based approaches have shown some success in designing multiobjective evolutionary algorithms (MEAs). Their methods of fitness assignment are mainly from the information of dominated and nondominated individuals. On the top of the hierarchy of MEAs, the strength Pareto evolutionary algorithm (SPEA) has been elaborately designed with this principle in mind. In this paper, we propose a (μ+λ) multiobjective evolutionary algorithm ((μ+λ) MEA), which discards the dominated individuals in each generation. The comparisons of the experimental results demonstrate that the (μ+λ) MEA outperforms SPEA on five benchmark functions with less computational efforts. |
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