Evaluating Memory Schemas in a Memetic Algorithm for the Quadratic Assignment Problem

Abstract

The use of memory schemas in metaheuristics has been an important technique to improve the performance of the algorithms in order to perform a better search in the solution space of a given problem. There are some techniques that are inherent to the structure used, like population in population-based metaheuristics, or the tabu list in Tabu Search. It is common that these techniques are used alone or combined, towards the construction of a better algorithm. In this work, we present the inclusion of several memory schemas in a Memetic Algorithm for the Quadratic Assignment Problem. The original MA algorithm already has good performance when compared with two other state of the art population-based metaheuristic algorithms. As a result of the memory schemas used, we were able to improve the quality of the solution generated by the Memetic Algorithm. The results are compared using the quality of the solutions and the success rate using the Copeland index.