Comparative Study of Path Planning by Particle Swarm Optimization and Genetic Algorithm

Hai Tao Han; Wan Feng Ji; Yao Qing Zhang; De Peng Sha

doi:10.4028/www.scientific.net/AMM.687-691.1420

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Comparative Study of Path Planning by Particle...

Comparative Study of Path Planning by Particle Swarm Optimization and Genetic Algorithm

Abstract:

Two main requirements of the optimization problems are included: one is finding the global minimum, the other is obtaining fast convergence speed. As heuristic algorithm and swarm intelligence algorithm, both particle swarm optimization and genetic algorithm are widely used in vehicle path planning because of their favorable search performance. This paper analyzes the characteristics and the same and different points of two algorithms as well as making simulation experiment under the same operational environment and threat states space. The result shows that particle swarm optimization is superior to genetic algorithm in searching speed and convergence.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1420-1424

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1420

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Online since:

November 2014

Authors:

Hai Tao Han*, Wan Feng Ji, Yao Qing Zhang, De Peng Sha

Keywords:

Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Threat Probability

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* - Corresponding Author

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