An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme

Date

2020-07-08

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

ACM

Type

Conference

Peer reviewed

Yes

Abstract

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem(MOP) into a number of single-objective subproblems. Penalty boundary intersection (PBI) in MOEA/D is one of the most popular decomposition approaches and has attracted significant attention. In this paper, we investigate two recent improvements on PBI, i.e. adaptive penalty scheme (APS) and subproblem-based penalty scheme (SPS), and demonstrate their strengths and weaknesses. Based on the observations, we further propose a hybrid penalty scheme (HPS), which adjusts the PBI penalty factor for each subproblem in two phases, to ensure the diversity of boundary solutions and good distribution of intermediate solutions. HPS specifies a distinct penalty value for each subproblem according to its weight vector. All the penalty values of subproblems increase with the same gradient during the first phase, and they are kept unchanged during the second phase.

Description

Keywords

Decomposition, Multiobjective evolutionary algorithm, Penalty boundary intersection, Adaptive penalty scheme, Subproblem-based penalty scheme, Hybrid penalty scheme

Citation

Guo, J., Shao, M., Jiang, S. and Yang, S. (2020) An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Electronic conference, July 2020.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)