Sports inspired computational intelligence algorithms for global optimization

摘要

Many classical search and optimization algorithms are especially insufficient in solving very hard large scale nonlinear problems with stringent constraints. Hence, computational intelligence optimization algorithms have been proposed and used to find well-enough solutions at a reasonable computation time when the classical algorithms are not applicable or do not provide good solutions to these problems due to the unmanageable search space. Many existing algorithms are nature-inspired, which work by simulating or modeling different natural processes. Due to the philosophy of continually searching the best and absence of the most efficient method for all types of problems, novel algorithms or new variants of current algorithms are being proposed and seem to be proposed in future to see if they can cope with challenging optimization problems. Studies on sports in recent years have shown that processes, concepts, rules, and events in various sports can be considered and modelled as novel efficient search and optimization methods with effective exploration capabilities in many cases, which are able to outperform existing classical and computational intelligence based optimization methods within different types of search spaces (Kashan in Appl Soft Comput 16:171–200, 2014; Bouchekara in Oper Res 1–57, 2017; Razmjooy in J Control Autom Electr Syst 1–22, 2016; Osaba et al. in Appl Intell 41(1):145–166, 2014a, Sci World J, 2014b). These novel and interesting sports based algorithms have shown to be more effective and robust than alternative approaches in a large number of applications. In this work, all of the computational intelligence algorithms based on sports and their applications have been for the first time searched and collected. Specific modelling of real sport games for computational intelligence algorithms and their novelties in terms of comparison with alternative existing algorithms for optimization have been reviewed with specific characteristics, computational implementation details and main applications capabilities, in the frame of hard optimization problems. Information is given about these search and optimization algorithms such as League Championship Algorithm, Soccer League Optimization, Soccer Game Optimization, Soccer League Competition Algorithm, Golden Ball Algorithm, World Cup Optimization, Football Optimization Algorithm, Football Game Inspired Algorithm, and Most Valuable Player Algorithm. Performance comparison of these sports based algorithms and other popular algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution within unconstrained global optimization benchmark problems with different characteristics has been performed for the first time. A general evaluation has also been discussed with further research directions.