Paper Info
Title
Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization
Abstract
We propose a novel hybrid algorithm named PSO-DE, which integrates particle swarm optimization (PSO) with differential evolution (DE) to solve constrained numerical and engineering optimization problems. Traditional PSO is easy to fall into stagnation when no particle discovers a position that is better than its previous best position for several generations. DE is incorporated into update the previous best positions of particles to force PSO jump out of stagnation, because of its strong searching ability. The hybrid algorithm speeds up the convergence and improves the algorithm's performance. We test the presented method on 11 well-known benchmark test functions and five engineering optimization functions. Comparisons show that PSO-DE outperforms or performs similarly to seven state-of-the-art approaches in terms of the quality of the resulting solutions.
Year
DOI
Venue
2010
10.1016/j.asoc.2009.08.031
Appl. Soft Comput.
Keywords
Field
DocType
engineering optimization problem,constrained optimization,engineering optimization function,novel hybrid algorithm,differential evolution,previous best position,particle swarm optimization,well-known benchmark test function,pso-de,state-of-the-art approach,traditional pso,hybridizing particle swarm optimization,hybrid algorithm speed,optimization problem,hybrid algorithm
Particle swarm optimization,Derivative-free optimization,Mathematical optimization,Meta-optimization,Differential evolution,Multi-swarm optimization,Artificial intelligence,Engineering optimization,Imperialist competitive algorithm,Mathematics,Machine learning,Metaheuristic
Journal
Volume
Issue
ISSN
10
2
Applied Soft Computing Journal
Citations 
PageRank 
References 
163
4.51
21
Authors
3
Search Limit
100163
Name
Order
Citations
PageRank
Hui Liu11654.87
Zixing Cai2152566.96
Yong Wang359625.79