Adaptive PBI for Massively Parallel MOEA/D in a Distributed Memory Environment
Yuji Sato, Tomoya Hirayama, Ryo Ikami · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
This paper proposes an adaptive PBI for massively parallel MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the reference vector in the objective function space, the T-neighbor is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a method to improve the problem that the T-neighbor is divided and the solution distribution becomes sparse by adaptively controlling the penalty value in the PBI function according to the distance from the reference vector using a distribution function such as Laplace distribution. The effectiveness of the proposed method is shown by comparison with execution using a single CPU.