title: Adaptive Replacement Strategies for MOEA/D.
abstract: 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.
authors.forename: Zhenkun
authors.initials: Z
authors.lastname: Wang
authors.forename: Qingfu
authors.initials: Q
authors.lastname: Zhang
authors.forename: Aimin
authors.initials: A
authors.lastname: Zhou
authors.forename: Maoguo
authors.initials: M
authors.lastname: Gong
authors.forename: Licheng
authors.initials: L
authors.lastname: Jiao