A New Fitness Based Adaptive Parameter Particle Swarm Optimizer

Abstract

Particle swarm optimization (PSO) is a stochastic global optimization approach whose search characteristics are controlled by three parameters, inertial weight w, cognitive parameter c1 and social parameter c2. Large parameter values facilitate exploration by searching new horizons of solution space. On the other hand, small parameter values facilitate exploitation by searching the neighborhood. An appropriate value of these parameters provides a balance between exploration and exploitation and results in better performance. An adaptive parameter PSO (AP-PSO) algorithm is proposed in this work where the inertial weight is gradually decreased and values of the cognitive and social parameters depend on the fitness values. Good fitness values support exploitation and poor fitness values support exploration. The proposed algorithm has shown excellent performance on low dimensional system identification problems as well as high dimensional articulated human tracking (AHT) problems.