A Hybrid NSGA-II Algorithm for Multiobjective Quadratic Assignment Problems

In this study, we propose a novel hybrid multiobjective evolutionary algorithm for solving multiobjective quadratic assignment problems. During the last decade, the researchers gave increasing attention to the multiobjective structure of quadratic assignment problems and developed and/or used several multi objective metaheuristics. The nondominated sorting genetic algorithm (NSGA-II) has been shown to solve various multiobjective problems much better than other recently-proposed constraint handling approaches. Besides, the effectiveness of conic scalarization method was also proven for solution of multiobjective problems, that have non-linear structure. Here, a hybrid multiobjective evolutionary algorithm (cNSGA-II) featured with NSGA-II and conic scalarization's Pareto solutions is developed to obtain as much Pareto points, as possible. To test the performance of the algorithm we have selected the test problems from the literature and compared the performances by well-known diameter metric. It has been shown that cNSGA-II is effective in solving multiobjective quadratic assignment problems.