Research Repository

Adaptive Replacement Strategies for MOEA/D

Wang, Zhenkun and Zhang, Qingfu and Zhou, Aimin and Gong, Maoguo and Jiao, Licheng (2016) 'Adaptive Replacement Strategies for MOEA/D.' IEEE Transactions on Cybernetics, 46 (2). pp. 474-486. ISSN 2168-2267

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Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decompose a multiobjective optimization problem into a set of simple optimization subproblems and solve them in a collaborative manner. A replacement scheme, which assigns a new solution to a subproblem, plays a key role in balancing diversity and convergence in MOEA/D. This paper proposes a global replacement scheme which assigns a new solution to its most suitable subproblems. We demonstrate that the replacement neighborhood size is critical for population diversity and convergence, and develop an approach for adjusting this size dynamically. A steady-state algorithm and a generational one with this approach have been designed and experimentally studied. The experimental results on a number of test problems have shown that the proposed algorithms have some advantages.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Mar 2016 12:35
Last Modified: 15 Jan 2022 00:47

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