A novel multi-objective evolutionary algorithm based on space partitioning

Date

2019-11

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

Springer

Type

Conference

Peer reviewed

Yes

Abstract

To design an e ective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs; 2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs; 3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion de ned in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks.

Description

Keywords

Multi-objective optimization, kd-tree space partitioning, Max-Min method

Citation

Wu, X., Li, C., Zeng, S. and Yang, S. (2019) A novel multi-objective evolutionary algorithm based on space partitioning. Proceedings of the 11th International Symposium on Intelligence Computation and Applications,

Rights

Research Institute