A NEW PARTICLE SWARM OPTIMIZATION METHOD FOR THE PATH PLANNING OF UAV IN 3D ENVIRONMENT
Particle swarm optimization (PSO) method is relatively a new population-based intelligence algorithm and exhibits good performance in optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged possibly around the local solution. PSO technique can be augmented with an additional mutation operator that provides diversity and helps prevent premature convergence on local optima. In this paper, mathematical analysis of a basic PSO is reissued and a diversity concept is evaluated in commonly used PSO algorithms including constriction factor PSO, inertial weight PSO, Gaussian mutation PSO, and a new vibrational mutation PSO combining the idea of mutation strategy related to periodicity. New algorithm is tested and compared with selected PSO algorithms. The comparative experiments have been conducted on a wide range of nonlinear functions and a path planning problem of unmanned aerial vehicle (UAV) in three-dimensional (3D) terrain environment. The results give insight into how mutation operator effects the nature of the diversity and show that the addition of a mutation operator with a periodicity concept can significantly enhance the optimization performance.
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