Paper Info
Title
Diversity-maintained differential evolution embedded with gradient-based local search
Abstract
Differential evolution (DE) has been used to solve real-parameter optimization problems with nonlinear and multimodal functions for more than a decade of years. However, it is pointed out that this classical DE harbors restricted efficiency and limited local search ability. Inspired by that gradient-based algorithms have powerful local search ability, we propose a new algorithm, which is diversity-maintained DE based on gradient local search (namely, DMGBDE), by incorporating approximate gradient-based algorithms into the DE search while maintaining the diversity of the population. The primary novelties of the proposed DMGBDE are the following: (1) the gradient-based algorithm is embedded into DE in a different manner and (2) a diversity-maintained mutation is introduced to slow down the learning procedure from the searched best individual. We conduct numerical experiments with a number of benchmark problems to measure the performance of the proposed DMGBDE. Simulation results show that the proposed DMGBDE outperforms classical DE and variant without gradient local search or diversity-based mutation. Moreover, comparison with some other recently reported approaches indicates that our proposed DMGBDE is rather competitive.
Year
DOI
Venue
2013
10.1007/s00500-012-0962-x
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Keywords
Field
DocType
ability to continue searching,differential evolution,diversity-maintained mutation,gradient local search
Population,Guided Local Search,Computer science,Line search,Artificial intelligence,Optimization problem,Iterated local search,Mathematical optimization,Algorithm,Beam search,Differential evolution,Local search (optimization),Machine learning
Journal
Volume
Issue
ISSN
17
8
14337479
Citations 
PageRank 
References 
10
0.46
34
Authors
3
Name
Order
Citations
PageRank
Wei-Cheng Xie19412.05
Wei Yu2100.46
Xiufen Zou327225.44