Fitness-Based Acceleration Coefficients to Enhance the Convergence Speed of Novel Binary Particle Swarm Optimization

Abstract

Acceleration coefficients are the key parameters of particle swarm optimization (PSO) algorithm used to control the movement of particles by modifying its cognitive and social components. Several variants have been proposed that modify the acceleration coefficients to improve the convergence speed of PSO in continuous search space. In this regard, a few attentions have been paid to improve the convergence speed of binary particle swarm optimization (BPSO). Moreover, in presence of distinct position of particles by ignoring the dispersion of particles in a search space, BPSO deals all particles equally. To address this issue, we have proposed a fitness-based acceleration coefficients Novel BPSO, called FAC-NBPSO. In the proposed algorithm, the fitness of each particle is used to modify the cognitive and social components of each particle. The performance of the proposed algorithm is tested on four benchmark test functions. The experimental results show that the proposed algorithm performs better than the compared algorithm with improved convergence speed.