Efficient coordinator guided particle swarm optimization for real-parameter optimization

Particle swarm optimization (PSO) is a stochastic optimization algorithm which usually suffers from local confinement losing its diversity. In this paper, we have proposed an efficient coordinator guided PSO (ECG-PSO), which provides a good diversity to the swarms maintaining good convergence speed and hence improves the fitness and robustness of the technique. We comprehensively evaluate the performance of the ECG-PSO by applying it on real-parameter benchmark optimization functions. Again, the result of comparison shows that ECG-PSO is more efficient compared to other PSO variants for solving complex problems.