Title :
Hybrid evolutionary algorithms based on PSO and GA
Author :
Shi, X.H. ; Lu, Y.H. ; Zhou, C.G. ; Lee, H.P. ; Lin, W.Z. ; Liang, Y.C.
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Abstract :
Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of the two proposed methods possess better ability to find the global optimum than that of the standard PSO algorithm.
Keywords :
artificial intelligence; genetic algorithms; parallel algorithms; artificial intelligence; genetic algorithm; hybrid evolutionary algorithm; parallel algorithm; particle swarm optimization; Algorithm design and analysis; Benchmark testing; Computational modeling; Computer science; Educational institutions; Evolutionary computation; Genetic algorithms; Genetic mutations; High performance computing; Particle swarm optimization;
Conference_Titel :
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN :
0-7803-7804-0
DOI :
10.1109/CEC.2003.1299387
