League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships

• Investigating the behavior of LCA under various setting for parameters.
• Examining the validity of updating equations and learning strategies followed in LCA.
• The algorithm is capable to find the global optimum in most of investigated problems.
• The algorithm behaves more constantly and reliable.

League Championship Algorithm (LCA) is a recently proposed stochastic population based algorithm for continuous global optimization which tries to mimic a championship environment wherein artificial teams play in an artificial league for several weeks (iterations). Given the league schedule in each week, a number of individuals as sport teams play in pairs and their game outcome is determined in terms of win or loss (or tie), given the playing strength (fitness value) along with the intended team formation/arrangement (solution) developed by each team. Modeling an artificial match analysis, each team devises the required changes in its formation (generation of a new solution) for the next week contest and the championship goes on for a number of seasons (stopping condition). An add-on module based on modeling the end season transfer of players is also developed to possibly speed up the global convergence of the algorithm. Extensive analysis to verify the rationale of the algorithm and suitability of the updating equations together with investigating the effect of different settings for the control parameters are carried out empirically on a large number of benchmark functions. Results indicate that LCA exhibits promising performance suggesting that its further developments and practical applications would be worth investigating in the future studies.

Figure optionsDownload as PowerPoint slide