PasMoQAP: A parallel asynchronous memetic algorithm for solving the Multi-Objective Quadratic Assignment Problem - Sanhueza, Claudio; Jimenez, Francia; Berretta, Regina; Moscato, Pablo (2017 IEEE Congress on Evolutionary Computation (CEC), 2017)
Multi-Objective Optimization Problems (MOPs) have attracted growing attention during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have been extensively used to address MOPs because are able to approximate a set of non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP which has been extensively studied, and used in several real-life applications. The mQAP is defined as having as input several flows between the facilities which generate multiple cost functions that must be optimized simultaneously. In this study, we propose PASMOQAP, a parallel asynchronous memetic algorithm to solve the Multi-Objective Quadratic Assignment Problem. PASMOQAP is based on an island model that structures the population by creating subpopulations. The memetic algorithm on each island individually evolve a reduced population of solutions, and they asynchronously cooperate by sending selected solutions to the neighboring islands. The experimental results show that our approach significatively outperforms all the island-based variants of the multi-objective evolutionary algorithm NSGA-II. We show that PASMOQAP is a suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.