Nonlinear Dissipative Particle Swarm Algorithm and Its Applications

Abstract

A nonlinear dissipative particle swarm algorithm is proposed for the problem of poor search accuracy by the particle swarm algorithm, especially for the optimization of high-dimensional functions. The algorithm dissipates the particles in a nonlinear increasing way: avoiding a large amount of unnecessary dissipation at the beginning of the iteration and putting more effort into dissipation at the end of the iteration, which improves the operation efficiency and global search ability of the algorithm. On this basis, the inertia weights are adjusted with a nonlinear decreasing strategy, which improves the search ability of the algorithm when there is no dissipation. The experimental results show that the nonlinear dissipative particle swarm algorithm has superior performance when dealing with high-dimensional function optimization problems and mobile robot path planning problems.