An Adaptive Particle Swarm Optimization Algorithm with Dynamic Nonlinear Inertia Weight Variation
Lei Gao · Science Technology and Engineering · 2011
The particle swarm optimization(PSO) is a relatively new generation of intelligent optimization algorithm which is based on a metaphor of social interaction,namely bird flocking or fish schooling.Although preexist PSO algorithms with linearly decreasing the inertia weight(LDI) have shown some important advances in convergence velocity and quality,its linear variation couldn't effectively simulate complicated nonlinear search behavior of the particle during iterations.There is still some problem in jumping from local optimal solution for these algorithms. A new improvement of PSO is proposed with dynamic nonlinear inertia weight(NDI) variation.A nonlinear exponent function is introduced to describe the dynamic variation character of inertia weight with swarm evolution.A suitability set of control parameters of the nonlinear function through numeric experiments is also presents.The performance of the proposed PSO algorithm is demonstrated by applying it for several benchmark problems and comparing it with results reported in literatures.It is clear that DNI particle swarm algorithm shows faster convergence velocity for uni-modal functions and better ability of jumping from getting into near optimal solution to better solution quality.