A Discrete Hybrid Grey Wolf Optimizer Algorithm to solve Quadratic Assignment Problem

Abstract

Grey Wolf Optimizer (GWO) is one of the popular swarm intelligence-based algorithms that derived its inspiration from the hunting behaviour of wolves. GWO has been found quite an effective algorithm for solving various optimization problems. It has demonstrated impressive results, which makes it a strong candidate for finding optimal solutions to NP-hard problems. This study proposes a discrete Hybrid Grey Wolf Optimizer (HGWO) that combines GWO with Tabu Search (TS) to address the well-known combinatorial optimization problem, the Quadratic Assignment Problem (QAP). The development of efficient techniques for the QAP is an active area of research, which motivates our attempts to propose HGWO. As QAP is a combinatorial optimization problem, the continuous values obtained from the classical GWO are converted to discrete values using the largest real value mapping. In HGWO, the position of each individual is first updated by GWO and further improved by TS. The performance of HGWO is evaluated through computational experiments over 100 QAP benchmark instances. The numerical results are compared with other well-known algorithms from the literature. Also, for an unbiased and rigorous comparison, statistical tests such as Friedman non-parametric test and Wilcoxon signed rank test are conducted. The analysis of obtained numerical results demonstrated that HGWO has merit in solving QAP.