Comparative Research on Particle Swarm Optimization and Genetic Algorithm | Zendy

Zhijie Li | Zendy; Xiangdong Liu | Zendy; Xiaodong Duan | Zendy; Feixue Huang | Zendy

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Comparative Research on Particle Swarm Optimization and Genetic Algorithm

Author(s) -

Zhijie Li,

Xiangdong Liu,

Xiaodong Duan,

Feixue Huang

Publication year - 2010

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v3n1p120

Subject(s) - computer science , particle swarm optimization , coding (social sciences) , meta optimization , algorithm , convergence (economics) , mathematical optimization , multi swarm optimization , genetic algorithm , process (computing) , global optimization , mathematics , machine learning , statistics , economics , economic growth , operating system

Genetic algorithm (GA) is a kind of method to simulate the natural evolvement process to search the optimal solution, and the algorithm can be evolved by four operations including coding, selecting, crossing and variation. The particle swarm optimization (PSO) is a kind of optimization tool based on iteration, and the particle has not only global searching ability, but also memory ability, and it can be convergent directionally. By analyzing and comparing two kinds of important swarm intelligent algorithm, the selecting operation in GA has the character of directivity, and the comparison experiment of two kinds of algorithm is designed in the article, and the simulation result shows that the GA has strong ability of global searching, and the convergence speed of PSO is very quick without too many parameters, and could achieve good global searching ability.

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