Performance analysis of parallel strategies for bi-objective network partitioning

Abstract

A basic characteristic of multi-objective optimization is the conflict among the different objectives. In most real optimization problems it is very difficult, even not possible, to obtain an unique solution that optimize all the objectives. Metaheuristics methods have become important tools for solving this kind of problems. Most of them use a population of solutions, thus implying runtime increases as the population size grows. The use of parallel processing is an useful tool to overcome this drawback. This paper analyzes the performance of several parallel paradigms in the multi-objective context. More specifically, we evaluate the performance of three parallel paradigms dealing with the Pareto Simulated Annealing algorithm for Network Partitioning.