An efficient many objective optimization algorithm with few parameters

Zhang，Qingquan1,2; Liu，Jialin1,2; Yao，Xin1,2,3

2023-12-01

10.1016/j.swevo.2023.101405

Swarm and Evolutionary Computation   影响因子和分区

2210-6502

During the past two decades, numerous many-objective optimization evolutionary algorithms (MaOEAs) have been proposed to tackle the challenges traditional multi-objective evolutionary algorithms face, that is to deal with abundant non-dominated solutions and low selection pressure. Specifically, a series of sophisticated selection strategies have been adopted, some of which need additional pre-defined parameters or a significant amount of extra computation time, which retards their applications in real life. In this paper, we propose an efficient indicator-based MaOEA with few parameters, SDE-MOEA, that uses an adaptive combination of commonly used selection methods to improve search efficiency based on SDE. SDE addresses situations where SDE cannot distinguish individuals with the same SDE values. The adaptive selection method dynamically selects between one-time and iterative selection methods at different stages of evolution to improve the search efficiency. Furthermore, apart from the four parameters for generating offspring, our proposed SDE-MOEA does not introduce additional parameters. We conduct experimental studies to compare SDE-MOEA with 11 state-of-the-art algorithms, including 2REA, I, θ-DEA, LMPFE, CVEA3, SRA, MOEA/DD, IBEA, Two_Arch2, NSGA-III, and SPEA2SDE using four representative performance indicators (HV, SP, PD and GD) on MaF benchmark with 5, 8, 10, and 15 objectives. Experimental studies demonstrate that, compared to the algorithms, SDE-MOEA achieves better HV and competitive convergence and uniformity performance, while requiring few parameters. It means that SDE-MOEA can find a solution set with better convergence and uniformity to help decision-makers understand the solved problems. Furthermore, SDE-MOEA loses little spreadability since a better convergence often leads to a worse spread.

Indicator-based selection Many-objective evolutionary algorithm Many-objective optimization Multi-criteria decision-making Multi-objective optimization

SCI ; EI

英语

第一 ; 通讯

WOS:001101587000001

20234314961205

Benchmarking ; Decision making ; Efficiency ; Evolutionary algorithms ; Iterative methods ; Parameter estimation

Management:912.2 ; Production Engineering:913.1 ; Optimization Techniques:921.5 ; Numerical Methods:921.6

2-s2.0-85174729594

Scopus

1.Research Institute of Trustworthy Autonomous System,Southern University of Science and Technology,Shenzhen,518055,China
2.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.CERCIA,School of Computer Science,University of Birmingham,Birmingham,B15 2TT,United Kingdom

Zhang，Qingquan,Liu，Jialin,Yao，Xin. An efficient many objective optimization algorithm with few parameters[J]. Swarm and Evolutionary Computation,2023,83.

Zhang，Qingquan,Liu，Jialin,&Yao，Xin.(2023).An efficient many objective optimization algorithm with few parameters.Swarm and Evolutionary Computation,83.

Zhang，Qingquan,et al."An efficient many objective optimization algorithm with few parameters".Swarm and Evolutionary Computation 83(2023).