IMPROVED ANTLION OPTIMIZATION ALGORITHM FOR QUADRATIC ASSIGNMENT PROBLEM

Abstract

The Antlion Optimization (ALO) algorithm is a meta-heuristic optimization algorithm based on the hunting of ants by antlions. The basic inadequacy of this algorithm is that it has long run time because of the random walk model used for the ant's movement. We improved some mechanisms in ALO algorithm, such as ants' random walking, reproduction, sliding ants towards antlion, elitism, and selection procedure. This proposed algorithm is called Improved Antlion Optimization (IALO) algorithm. To show the performance of the proposed IALO algorithm, we used different measurement metrics, such as mean best, standard deviation, optimality, accuracy, CPU time, and number of function evaluations (NFE). The proposed IALO algorithm was tested for different benchmark test functions taken from the literature. There are no studies regarding time analysis of ALO algorithm found in the literature. This study firstly aims to demonstrate the success of the proposed IALO algorithm especially in runtime analysis. Secondly, the IALO algorithm was also applied to the Quadratic Assignment Problem (QAP) known as a difficult combinatorial optimization problem. In QAP tests, the performance of the IALO algorithm was compared with the performances of the classic ALO algorithm and 14 well-known and recent meta-heuristic algorithms. The results of the benchmark test functions show that IALO algorithm is able to provide very competitive results in terms of mean best/standard deviation, optimality, accuracy, CPU time and NFE metrics. The CPU time results prove that IALO algorithm is faster than the classic ALO algorithm. As a result of the QAP tests, the proposed IALO algorithm has the best performance according to the mean cost, worst cost and standard deviation. The source codes of QAP with the proposed IALO algorithm are publicly available at https://github.com/uguryuzgec/QAP-with-IALO.