Cooperative Coevolution with Two-Stage Decomposition for Large-Scale Global Optimization Problems

Abstract

Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems.

Dominance-based variable analysis for large-scale multi-objective problems

“…3.1). Decomposing large scale optimization problems using several grouping methods are common to control group sizes and interaction sensitivity (e.g., Zhenyu et al 2008;Yue and Sun 2021), or increase grouping efficiency (e.g., Irawan et al 2020). The two-stage grouping in MOEA/DVA differs in that the first stage is to address the multi-objective nature of the problem.…”