RT info:eu-repo/semantics/article
T1 Comparison between single and multi-objective evolutionary algorithms to solve the knapsack problem and the travelling salesman problem
A1 León Hernández, Coromoto
A1 Mahrach, Mohammed
A1 Miranda Valladares, Gara
A1 Segredo González, Eduardo Manuel
A2 Ingeniería Informática y de Sistemas
A2 Algoritmos y lenguajes paralelos
K1 multi-objective optimization
K1 single-objective optimization
K1 evolutionary algorithm
K1 knapsack problem
K1 travelling salesman problem
AB One of the main components of most modern Multi-Objective Evolutionary Algorithms (MOEAs) is to maintain a proper diversity within a population in order to avoid the premature convergence problem. Due to this implicit feature that most MOEAs share, their application for Single-Objective Optimization (SO) might be helpful, and provides a promising field of research. Some common approaches to this topic are based on adding extra—and generally artificial—objectives to the problem formulation. However, when applying MOEAs to implicit Multi-Objective Optimization Problems (MOPs), it is not common to analyze how effective said approaches are in relation to optimizing each objective separately. In this paper, we present a comparative study between MOEAs and Single-Objective Evolutionary Algorithms (SOEAs) when optimizing every objective in a MOP, considering here the bi-objective case. For the study, we focus on twowell-known and widely studied optimization problems: the Knapsack Problem (KNP) and the Travelling Salesman Problem (TSP). The experimental study considers three MOEAs and two SOEAs. Each SOEAisapplied independently for each optimization objective, such that the optimized values obtained for each objective can be compared to the multi-objective solutions achieved by the MOEAs. MOEAs,however, allow optimizing two objectives at once, since the resulting Pareto fronts can be used to analyze the endpoints, i.e., the point optimizing objective 1 and the point optimizing objective 2. The experimental results show that, although MOEAs have to deal with several objectives simultaneously, they can compete with SOEAs, especially when dealing with strongly correlated or large instances.
YR 2020
FD 2020
LK http://riull.ull.es/xmlui/handle/915/40616
UL http://riull.ull.es/xmlui/handle/915/40616
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 08-ene-2025