Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 46, Issue 2, Pages 474-486Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2403849
Keywords
Adaptive scheme; decomposition; multiobjective optimization; replacement
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Funding
- Cheung Kong Scholars Programme of China [K5051302050]
- National Natural Science Foundation of China [61473241, 61273317, 61273313, 61175063]
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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.
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