MOEA/D Algorithm Based on the Hybrid Framework for Multi-objective Evolutionary Algorithm

Authors

Hongjun Tian, 1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;;2. Postdoctoral Research Station of Shenwan Hongyuan Secur Co Ltd. and Fudan University, Shanghai 200031, China;
Wang Lei, 1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;;
Qidi Wu, 1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;;

Abstract

Abstract: Aimto the difficulties of designing the bonding mechanism of global optimization algorithm and local search strategy for hybrid multi-objective evolutionary algorithm, and of improving the performance of multi-objective evolutionary algorithms, based on the feedback control idea, a systematic and modular hybrid MOEA/D algorithm combining the global optimization and local search is proposed. In the algorithm, a diversity measure method based on crowded entropy is designed; a local search strategy based on simplified quadratic approximation and population diversity enhancement strategy for MOEA/D is proposed. The numerical experiments show that the proposed HMOEA/D can achieve a balance between diversity and convergence of algorithm. The proposed hybrid framework can effectively improve the performance of existing multi-objective evolutionary algorithms.

Recommended Citation

Tian, Hongjun; Lei, Wang; and Wu, Qidi (2020) "MOEA/D Algorithm Based on the Hybrid Framework for Multi-objective Evolutionary Algorithm," Journal of System Simulation: Vol. 32: Iss. 2, Article 7.
DOI: 10.16182/j.issn1004731x.joss.17-9183
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss2/7

First Page

201

Revised Date

2018-06-06

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-9183

Last Page

216

CLC

TP18

Recommended Citation

Tian Hongjun, Wang Lei, Wu Qidi. MOEA/D Algorithm Based on the Hybrid Framework for Multi-objective Evolutionary Algorithm[J]. Journal of System Simulation, 2020, 32(2): 201-216.

DOI

10.16182/j.issn1004731x.joss.17-9183