Improved Antlion Optimization Algorithm via Tournament Selection

Abstract

From the measurement point of view, it is observed that antlion optimization algorithm (ALO) runs slower than other heuristic algorithms and it needs to be improved in terms of optimality and accuracy. For this reason, improved antlion optimization algorithm via tournament selection (IALOT) is presented in this study. IALOT, ALO, particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms have been evaluated using benchmark test functions such as time, optimality, accuracy, CPU time, number of function evaluations (NFE), mean best solution and standard deviation. In summary, elite antlion selection, random walks, and other parts of the antlion optimization algorithm have been developed. As a result, the IALOT algorithm has shown better results than ALO algorithm.