Paper Info
Title
Modified differential evolution algorithm using a new diversity maintenance strategy for multi-objective optimization problems
Abstract
In this paper, we propose a modified differential evolution (DE) based algorithm for solving multi-objective optimization problems (MOPs). The proposed algorithm, called multi-objective DE with dynamic selection mechanism (DSM), i.e., MODE-DSM, modifies the general DE mutation operation to produce a population at each generation. To determine and evaluate a better spread of the non-dominated solution, a DSM with a new cluster degree measure is developed. The DSM is also used to select diverse non-dominated solutions. The performance of the proposed algorithm is evaluated against seventeen bi-objective and two tri-objective benchmark test problems. The experimental results show that the proposed algorithm achieves better convergence to the Pareto-optimal front as well as better diversity on the final non-dominated solutions than the other five multi-objective evolutionary algorithms (MOEAs). It suggests that the proposed algorithm is promising in dealing with MOPs. The ability of MODE-DSM with small population and the sensitivity of MODE-DSM have also been experimentally investigated in this paper.
Year
DOI
Venue
2015
10.1007/s10489-014-0619-9
Applied Intelligence
Keywords
Field
DocType
Differential evolution,Multi-objective optimization problems,Non-dominated,Pareto-optimal front
Convergence (routing),Population,Mathematical optimization,Evolutionary algorithm,Computer science,Meta-optimization,Differential evolution,Multi-objective optimization,Optimization problem,Differential evolution algorithm
Journal
Volume
Issue
ISSN
43
1
0924-669X
Citations 
PageRank 
References 
9
0.45
32
Authors
5
Name
Order
Citations
PageRank
Bili Chen1191.32
Yangbin Lin2655.11
Wenhua Zeng313614.83
Defu Zhang465752.80
Yain-Whar Si514328.41