Paper Info
Title
Comparison between Particle Swarm Optimization, Differential Evolution and Multi-Parents Crossover
Abstract
Particle swarm optimization (PSO), differential evolu- tion (DE) and multi-parents crossover (MPC) are the evo- lutionary computation paradigms, all of which have shown superior performance on complex non-linear function op- timization problems. This paper detects the underlying re- lationship between them and then qualitatively proves that these heuristic approaches from different theoretical prin- ciples are consistent in form. Comparison experiments in- volving eight test functions well studied in the evolutionary optimization literature are used to highlight some perfor- mance differences between the techniques. The results from our study show that DE generally outperforms the other al- gorithms.
Year
DOI
Venue
2007
10.1109/CIS.2007.37
CIS
Keywords
Field
DocType
multi-parents crossover,comparison experiment,different theoretical prin,differential evolu,complex non-linear function op,heuristic approach,evolutionary optimization literature,mance difference,differential evolution,lutionary computation paradigm,particle swarm optimization,security,software engineering,evolutionary computation,genetics,testing,computational intelligence,algorithm design and analysis
Particle swarm optimization,Heuristic,Mathematical optimization,Algorithm design,Crossover,Computational intelligence,Computer science,Evolutionary computation,Differential evolution,Artificial intelligence,Machine learning,Computation
Conference
ISBN
Citations 
PageRank 
978-0-7695-3072-7
5
0.49
References 
Authors
3
2
Name
Order
Citations
PageRank
Xing Xu1204.05
Yuanxiang Li224551.20