A Self-Adaptive Particle Swarm Optimization Algorithm with Individual Coefficients Adjustment

This paper introduces a novel self-adaptive strategy of inertia weight and social acceleration coefficient adjustment in particle swarm optimization (PSO- SAIC). In PSO-SAIC, each particle has its individual inertia weight and social acceleration coefficient, which will be adjusted dynamically and self-adaptively by the result of the passed evolutions, so the PSO-SAIC can retain the diversity of particles . The result of the compare to the time-varying inertia weight particle swarm optimization and the time-varying acceleration coefficient particle swarm optimization with 3 classical benchmark functions shows that the PSO-SAIC provides outstanding global and local convergence performances in optimization high dimensional objects.