PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem

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 sub-populations. 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.Comment: 8 pages, 3 figures, 2 tables. Accepted at Conference on Evolutionary Computation 2017 (CEC 2017

An empirical comparison of memetic algorithm strategies on the multiobjective quadratic Assignment Problem

Evolutionary algorithm based metaheuristics have gained prominence in recent years for solving multiobjective optimization problems. These algorithms have a number of attractive features, but the primary motivation for many in the community is rooted in the use of a population inherent to evolutionary algorithms, which allows a single optimization run to provide a diverse set of nondominated solutions. However, for many combinatorial problems, evolutionary algorithms on their own do not perform satisfactorily. For these problems, the addition of a local search heuristic can dramatically improve the performance of the algorithms. Often called memetic algorithms, these techniques introduce a number of additional parameters which can require careful tuning. In this work, we provide an empirical comparison of a number of strategies for the construction of multiobjective memetic algorithms for the multiobjective quadratic assignment problem (mQAP), and provide a more principled analysis of those results using insights gained from analysis of the fitness landscape properties of the different problem instances. ©2009 IEEE