A multi-objective network design for real traffic models of the internet by means of a parallel framework for solving NP-hard problems

Abstract

The design and optimization of a communication network is a complex task, because it involves a lot of factors. The two most important factors are the network cost and the communications delay. We have tackled this NP-hard multi-objective optimization problem using two metaheuristics not used before: NSGA-II and SPEA-II, demonstrating that SPEA-II offers better results than NSGA-II. In order to facilitate the programming tasks, we have used the PISA framework. This paper proposes also a novel PISA parallelization based on MPI that allows distributing work load between computer cluster nodes, increasing thus the productivity.Communication networks; multio-objetive optimization; metaheuristics; PISA framework; parallelism.

A gravitational search algorithm for solving the relay node placement problem in wireless sensor networks

“…For MO‐GSA, it is the mutation probability. The procedure is as follows: we start from a default configuration, fixing the parameters one by one in their optimal values through a parametric sweep . Thus, the parameters of NSGA‐II and SPEA2 take a same value of 0.5, and the parameter of MO‐GSA assumes a value of 0.4.…”