Hybridized moth search algorithm for constrained optimization problems

In this paper, hybridized moth search algorithm adjusted for tackling constrained optimization problems, is presented. The moth search algorithm is new optimization method. The application of this approach for classic constrained functions was not published in any scientific paper before. By analyzing basic moth search, we noticed some deficiencies. To address these deficiencies, we hybridized the moth search algorithm with artificial bee colony metaheuristics. In order to evaluate robustness, convergence speed and solutions' quality of the hybridized moth search algorithm, we conducted tests on set of 13 classic constrained benchmarks. We performed comparative analysis with the original moth search, and with other algorithms, that proved to be robust and efficient optimization methods. According to the results obtained during experiments, and comparative analysis with other approaches, we came to the conclusion that the hybridized moth search algorithm has potential in dealing with constrained functions optimization.