Multi-objective Quadratic Assignment Problem: An Approach Using a Hyper-Heuristic Based on the Choice Function

Abstract

The Quadratic Assignment Problem (QAP) is an example of combinatorial optimization problem and it belongs to NP-hard class. QAP assigns interconnected facilities to locations while minimizing the cost of transportation of the flow of commodities between facilities. Hyper-Heuristics (HH) is a high-level approach that automatically selects or generates heuristics for solving complex problems. In this paper is proposed the use of a selection HH to solve the multi-objective QAP (mQAP). This HH is based on the MOEA/DD (Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition) and Choice Function strategy. The heuristics selected by HH correspond to the operators that generate new solutions in an iteration of the multi-objective evolutionary algorithm. IGD metric and statistical tests are applied in order to evaluate the algorithm performances in 22 mQAP instances. The effectiveness of the proposed method is shown and it is favorably compared with three other evolutionary multi-objective algorithms: IBEA, SMS-EMOA e MOEAD/DRA.