League Championship Algorithm: A New Algorithm for Numerical Function Optimization

Abstract

Inspired by the competition of sport teams in a sport league, an algorithm is presented for optimizing nonlinear continuous functions. A number of individuals as sport teams compete in an artificial league for several weeks (iterations). Based on the league schedule in each week, teams play in pairs and the outcome is determined in terms of win or loss, given known the team’s playing strength (fitness value) resultant from a particular team formation (solution). In the recovery period, each team devises the required changes in the formation/playing style (a new solution) for the next week contest and the championship goes on for a number of seasons (stopping condition). Performance of the proposed algorithm is tested in comparison with that of particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmarked functions. Results testify that the new algorithm performs well on all test problems, exceeding or matching the best performance obtained by PSO. This suggests that further developments and practical applications of the proposed algorithm would be worth investigating in the future.