Mentoring based particle swarm optimization algorithm for faster convergence

This paper presents a new particle swarm optimization (PSO) algorithm incorporating the concept of mentoring in the learning process for finding the optimum solution, referred to as a Mentoring based Particle Swarm Optimization (MePSO) algorithm. In human learning principles, mentoring provides both self and social cognizance through guidance, direction and momentum for the learners. Such a mentoring concept is integrated in PSO for faster convergence where all the particles are divided into dynamically changing three groups, namely, mentors, mentees and independent learners. In a given iteration, the particles classified as the mentees are supported by the mentor particles for searching in the potential region and eventually diverting the search towards the optimum solution. All the remaining particles perform independent search by employing social-awareness of the global search direction for intelligent exploration of the solution space. The proposed MePSO algorithm has been evaluated using 15 benchmark test functions from CEC2013 and the performance has been compared with different variants of PSO algorithms reported in the literature. The comparative results and the statistical analysis clearly indicate that MePSO performs better with faster convergence characteristics.