An analysis of control parameters of MOEA/D under two different optimization scenarios

Tanabe, Ryoji; Ishibuchi, Hisao

2018-09

10.1016/j.asoc.2018.05.014

APPLIED SOFT COMPUTING   影响因子和分区

1568-4946

1872-9681

An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evolutionary algorithms( MOEAs) in recent studies. A recent benchmarking study also shows that decomposition-based MOEA (MOEA/D) is competitive with state-of-the-art MOEAs when the UEA is incorporated into MOEA/D. However, a parameter study of MOEA/D using the UEA has not yet been performed. Thus, it is unclear how control parameter settings influence the performance of MOEA/D with the UEA. In this paper, we present an analysis of control parameters of MOEA/D under two performance evaluation scenarios. One is a final population scenario where the performance assessment of MOEAs is performed based on all nondominated solutions in the final population, and the other is a reduced UEA scenario where it is based on a pre-specified number of selected nondominated solutions from the UEA. Control parameters of MOEA/D investigated in this paper include the population size, scalarizing functions, and the penalty parameter of the penalty-based boundary intersection (PBI) function. Experimental results indicate that suitable settings of the three control parameters significantly depend on the choice of an optimization scenario. We also analyze the reason why the best parameter setting is totally different for each scenario. (C) 2018 Elsevier B.V. All rights reserved.

Multi-objective optimization Decomposition based evolutionary algorithms Parameter study External archive

SCI ; EI

英语

第一 ; 通讯

Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications

WOS:000443296000003

ELSEVIER

20182105225882

Control system analysis ; Function evaluation ; Multiobjective optimization ; Parameter estimation ; Population statistics

Control Systems:731.1 ; Optimization Techniques:921.5 ; Numerical Methods:921.6

COMPUTER SCIENCE

Web of Science

Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China

Tanabe, Ryoji,Ishibuchi, Hisao. An analysis of control parameters of MOEA/D under two different optimization scenarios[J]. APPLIED SOFT COMPUTING,2018,70:22-40.

Tanabe, Ryoji,&Ishibuchi, Hisao.(2018).An analysis of control parameters of MOEA/D under two different optimization scenarios.APPLIED SOFT COMPUTING,70,22-40.

Tanabe, Ryoji,et al."An analysis of control parameters of MOEA/D under two different optimization scenarios".APPLIED SOFT COMPUTING 70(2018):22-40.