Relegation-Based League Championship Algorithm

Abstract

Classical optimization techniques often prove insufficient in case of large scale combinatorial problems and non-linear problems. Consequently, heuristic based optimization techniques have been introduced. Recently, a novel metaheuristic named league championship algorithm (LCA) has been proposed for global optimization in continuous search space. LCA is a population based algorithm which mimics a sports league with a fixed number of teams. These teams, denoting solutions, compete against each other according to a predetermined schedule and the winner is determined on the basis of playing strength of the teams. The formations of the teams keep on improving throughout the season and at the end an optimal solution is obtained. Since, its inception, LCA has been employed for solving many optimization problems. However, there are few limitations of LCA in form of premature convergence and slow convergence rate. This paper attempts to overcome the shortcomings of LCA by proposing relegation based LCA which incorporates the concept of relegation into the original LCA. Comparative experiments have been performed on 10 different test functions and promising results are obtained. Additionally, a study is also done to investigate the impact of control parameters on the performance of relegation based LCA.