Tournament selection based antlion optimization algorithm for solving quadratic assignment problem

We propose and develop an improved version of antlion optimizer (ALO), namely tournament selection based antlion optimization algorithm for quadratic assignment problem (QAP). ALO algorithm has some handicaps, such as long run time, local optima stagnation and premature convergence for some problems. The literature describes different methods that improve the performance of antlion optimizer, but most of these are about specific optimization problems. In this paper, we introduce the tournament selection method instead of the roulette wheel method on random walking mechanism of ALO and we update some equations used in ALO algorithm. To compare the proposed tournament selection based ALO (TALO) algorithm with classic ALO, we deal with ten benchmark functions from literature. The comparison results are evaluated according to the different metrics, such as mean best, standard deviation, optimality, accuracy, CPU time, number of function evaluations (NFE). The detailed analyzes of the proposed TALO algorithm are performed. These are the convergence analysis, statistical analysis, search history analysis, trajectory analysis, average distance analysis, computational complexity analysis. The proposed TALO algorithm is compared with the other ALO versions (binary ALO and chaotic ALO variants) for same ten benchmark functions. As last, TALO algorithm has been also implemented for the quadratic assignment problem (QAP). The QAP results has been compared with several well-known meta-heuristic algorithms. TALO's performance has been evaluated with those of binary ALO and chaotic ALO variants for same QAP instance. Finally, the solution quality of proposed TALO algorithm has been analyzed for solving QAP using some instances presented in QAPLIB site. The results provide the proposed TALO algorithm has the best performance in comparison with those of the other meta-heuristic algorithms. Due to the performance of TALO algorithm, we expect this version to be applied for different optimization problems. (C) 2018 Karabuk University. Publishing services by Elsevier B.V.