An Efficient Grey Wolf Optimizer with Opposition-Based Learning and Chaotic Local Search for Integer and Mixed-Integer Optimization Problems

Determining the global optima of integer and mixed-integer nonlinear problems is a useful contribution in various engineering applications. Swarm intelligence is a well-known branch of nature-inspired algorithms which tries to determine the solution with the help of intelligent and collective behaviour of social creatures. Grey wolf optimizer (GWO) is one of the recently developed efficient algorithms which are quite popular nowadays. In the present study, first, the GWO is proposed for solving integer and mixed-integer optimization problems, and secondly, an improved version of GWO named IMI-GWO is proposed. The IMI-GWO attempts to alleviate from the major issues of premature convergence and slow convergence of classical GWO. In IMI-GWO, the opposition-based learning maintains the diversity and the chaotic search locally exploits the regions around the best solutions. To evaluate the performance of IMI-GWO, a set of 16 integer and mixed-integer problems and two engineering application problems, namely gear train and pressure vessel design problems, have been considered. The performance of the IMI-GWO is compared with other algorithms which are applied to solve these problems in the literature and with some recent algorithms. The comparison illustrates the better performance of the proposed algorithm.