A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization |
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| Affiliation: | 1. College of Computer Science, Shaanxi Normal University, Xi’an 710062, China;2. School of Computer Science and Technology, Xidian University, Xi’an 710071, China;1. Département génie électrique, Ecole Mohamamdia d’Ingénieurs (EMI), Université Mohammed V Agdal, Rabat, Morocco;2. Laboratoire de Recherche en Economie de l’Energie, Environnement et Ressources, Département d’Economie, University Caddy Ayyad, Marrakech, Morocco;1. College of Mathematics, Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China;2. College of Engineering, Shaoxing University, Shaoxing 312000, China;1. College of Finance, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China;2. School of Economics and Management, Southeast University, Nanjing 210096, Jiangsu, China;1. Simsoft Computer Technologies, Middle East Technical University, Teknokent Bolgesi, 06800 Ankara, Turkey;2. Microsoft, 1 Microsoft Way, Redmond, WA 98052, United States;3. Computer Engineering, Middle East Technical University, 06800 Ankara, Turkey;1. Centre for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, Skudai, Malaysia;2. Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia |
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| Abstract: | For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence. |
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| Keywords: | Multi-objective optimization Decomposition Uniform design Weight vector Many-objective optimization problems |
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