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

• Published in: Applied Mechanics and Materials Vol. 687-691; no. Manufacturing Technology, Electronics, Computer and Information Technology Applications; pp. 1420 - 1424
• Main Authors: Ji, Wan Feng, Han, Hai Tao, Zhang, Yao Qing, Sha, De Peng
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.11.2014
• Subjects: Algorithms; Computer simulation; Convergence; Genetic algorithms; Optimization; Path planning; Searching; Swarm intelligence; Genetic Algorithm (GA); Particle Swarm Optimization (PSO); Threat Probability
• ISBN: 3038353280; 9783038353287
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.687-691.1420

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.