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The effects of asymmetric neighborhood assignment in the MOEA/D algorithm |
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| Affiliation: | 1. School of Management, Hefei University of Technology, Hefei 230009, PR China;2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, PR China;3. Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07012, USA;1. Department of Computation and I.T., Simón Bolívar University, 1080A Caracas, Venezuela;2. CAMYTD, FACYT, University of Carabobo, Venezuela;3. Intelligent Databases and Information Systems Research Group, Department of Computer Science and A.I., University of Granada, 18071 Granada, Spain;1. Department of Maritime Information and Technology, National Kaohsiung Marine University, Kaohsiung 80543, Taiwan;2. Innovative Information Industry Research Center, Harbin Institute of Technology, Shenzhen Graduate School, Guangdong, China;3. Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. Department of Mechanical Engineering, Graphic Era Hill University, Dehradun 248001, India;2. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad 211004, India |
| Abstract: | The Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) is a very efficient multiobjective evolutionary algorithm introduced in recent years. This algorithm works by decomposing a multiobjective optimization problem to many scalar optimization problems and by assigning each specimen in the population to a specific subproblem. The MOEA/D algorithm transfers information between specimens assigned to the subproblems using a neighborhood relation.In this paper it is shown that parameter settings commonly used in the literature cause an asymmetric neighbor assignment which in turn affects the selective pressure and consequently causes the population to converge asymmetrically. The paper contains theoretical explanation of how this bias is caused as well as an experimental verification. The described effect is undesirable, because a multiobjective optimizer should not introduce asymmetries not present in the optimization problem. The paper gives some guidelines on how to avoid such artificial asymmetries. |
| Keywords: | Multiobjective optimization Evolutionary algorithms MOEA/D algorithm Selective pressure |
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