Title :
Comparison between differential evolution and particle swarm optimization algorithms
Author :
Dan Zhang ; Bin Wei
Author_Institution :
Dept. of Automotive, Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada
Abstract :
In this paper, the performance of differential evolution (DE) and particle swarm optimization (PSO) algorithms are compared and evaluated. The comparison is performed on eight benchmark functions f1-f8. New findings have been discovered for the PSO algorithm and the comparison results in this report show that DE generally is better than PSO in term of solution accuracy and robustness in almost all the problems. Generally, from the numerical results and graphic illustrations, we can demonstrate that DE is more efficient and robust compare to PSO, although PSO gives good results in some cases.
Keywords :
algorithm theory; particle swarm optimisation; robust control; PSO algorithms; differential evolution; particle swarm optimization algorithms; robustness; Algorithm design and analysis; Benchmark testing; Optimization; Particle swarm optimization; Sociology; Statistics; differential evolution (DE); optimization algorithm; particle swarm optimization (PSO);
Conference_Titel :
Mechatronics and Automation (ICMA), 2014 IEEE International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4799-3978-7
DOI :
10.1109/ICMA.2014.6885702