Fuzzy Dynamic Adaptation Particle Swarm Optimization for Global Optimization

Today the Particle swarm optimization is used to solve a large variety of nonlinear optimization problems. PSO has deficiencies with premature convergence and easy trapping into local optimum because its performance strongly depends on the choice of its settings i.e., inertia, cognitive and social factors. This paper presents a new PSO based on a fuzzy interference system to overcome this deficiencies. Proposed algorithm called Fuzzy Dynamic Adaptation Particle Swarm Optimization (FDAPSO). The algorithm s performance is tested through typical Benchmark functions. The experiment results and analysis show that it obviously enhances the performance of PSO.