Abstract：Concerning the problem that the independent adjusting strategy of inertia weight and learning factor reduces evolution unity and intelligence feature of Particle Swarm Optimization (PSO) algorithm, and cannot adapt to the complex and nonlinear optimization problems, a new PSO algorithm with learning factor controlled by inertia weight function was proposed. Learning factor in the presented PSO was regarded as inertia weight's linear, nonlinear or trigonometric function, and increased or decreased progressively when inertia weight decreased by degrees linearly or nonlinearly. This strategy could effectively enhance the interaction of inertia weight and learning factor, then balance the global exploration and local exploitation and preferably lead particles to search globally optimal solution. Then the inertia weights were given to linear and nonlinear methods to analyze fusion performance of weight and learning factor, and the experimental results to test functions show that nonlinear weight is better. Finally, the experimental simulation results on benchmark test functions and the comparison with PSO with asynchronous linear and trigonometric function learning factor draw a conclusion that the strategy uses inertia weight to adjust learning factor, balances particle learning ability of individual and population, and improves optimization precision of algorithm.
2013, Vol. 33 
