A modified competitive swarm optimizer for large scale optimization problems

Abstract

In the recent literature a popular algorithm namely ‘Competitive Swarm Optimizer (CSO)’ has been proposed for solving unconstrained optimization problems that updates only half of the population in each iteration. A modified CSO (MCSO) is being proposed in this paper where two thirds of the population swarms are being updated by a tri-competitive criterion unlike CSO. A small change in CSO makes a huge difference in the solution quality. The basic idea behind the proposition is to maintain a higher rate of exploration to the search space with a faster rate of convergence. The proposed MCSO is applied to solve the standard CEC2008 and CEC2013 large scale unconstrained benchmark optimization problems. The empirical results and statistical analysis confirm the better overall performance of MCSO over many other state-of-the-art meta-heuristics, including CSO. In order to confirm the superiority further, a real life problem namely ‘sampling-based image matting problem’ is solved. Considering the winners of CEC 2008 and 2013, MCSO attains the second best position in the competition.