Multi-objective memetic optimization for the bi-objective obnoxious p-median problem

We propose a Multi-Objective Memetic Algorithm (MOMA) to obtain high-quality approximations to the efficient front of the bi-objective obnoxious p-median problem, denoted as Bi-OpM. In particular, we introduce efficient crossover and mutation mechanisms. Additionally, we present several multi-objective local search methods. All the strategies are finally incorporated in a memetic algorithm which limits the search to the feasible region, thus performing an efficient exploration of the solutions space. Our experimentation compares several state-of-the-art procedures with the introduced MOMA emerging as the best performing method in all considered multi-objective metrics.