Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling

Abstract

In this paper, we consider the parallel machine scheduling which is combinatorial optimization problem. An improved meta-heuristic method is proposed to solve this problem. Antlion optimization algorithm (ALO) is one of the new meta-heuristic optimization algorithms and it is inspired by the hunting mechanism of antlion in nature. The original algorithm has some deficiencies like the runtime. Some improvements on the classic ALO algorithm are presented in this study. One of these improvements is to use the tournament method instead of the roulette wheel method in the selection of the antlion from the population for the random walk which is the most important mechanism on the original ALO algorithm. Some mechanisms in the original ALO algorithm have been improved, such as ant random walking, reproduction, sliding of ants towards antlion, elitism and selection procedure. There is no time analysis result about ALO algorithm in the literature. Due to this reason, it is aimed to show the performance of the proposed IALOT algorithm especially in runtime analysis. The proposed improved ALO algorithm via tournament selection method (IALOT) has been compared with the other popular well-known meta-heuristic algorithms on the multi-dimensions benchmark test functions and the Parallel Machine Scheduling (PMS) problem to evaluate its performance. The obtained results show that the IALOT has the best performance in terms of the optimality, accuracy and mean best metrics for the benchmark tests. According to the PMS results, the proposed IALOT algorithm is the best algorithm in comparison with the other meta-heuristic algorithms.