RT Journal Article
T1 InDM2: interactive dynamic multi-objective decision making using evolutionary algorithms
A1 Nebro-Urbaneja, Antonio Jesús
A1 Ruiz-Mora, Ana Belén
A1 Barba-González, Cristóbal
A1 García-Nieto, José Manuel
A1 Luque-Gallego, Mariano
A1 Aldana-Montes, José Francisco
K1 Computación evolutiva
AB Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi-objective optimization with preference articulation are still scarce. In this paper, we propose a new dynamic multi-objective optimization algorithm called InDM2 that allows the preferences of the decision maker (DM) to be incorporated into the search process. When solving a dynamic multi-objective optimization problem with InDM2, the DM can not only express her/his preferences by means of one or more reference points (which define the desired region of interest), but these points can be also modified interactively. InDM2 is enhanced with methods to graphically display the different approximations of the region of interest obtained during the optimization process. In this way, the DM is able to inspect and change, in optimization time, the desired region of interest according to the information displayed. We describe the main features of InDM2 and detail how it is implemented. Its performance is illustrated using both synthetic and real-world dynamic multi-objective optimization problems.
PB Elsevier
YR 2018
FD 2018-05-24
LK https://hdl.handle.net/10630/36558
UL https://hdl.handle.net/10630/36558
LA eng
NO Antonio J. Nebro, Ana B. Ruiz, Cristóbal Barba-González, José García-Nieto, Mariano Luque, José F. Aldana-Montes, InDM2: Interactive Dynamic Multi-Objective Decision Making Using Evolutionary Algorithms, Swarm and Evolutionary Computation, Volume 40, 2018, Pages 184-195, ISSN 2210-6502, https://doi.org/10.1016/j.swevo.2018.02.004.
NO This work is partially funded by Grants TIN2017-86049-R, TIN2014-58304 and ECO2014-56397-P (Ministerio de Ciencia e Innovación), and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz I+D+I). Cristóbal Barba-González is supported by Grant BES-2015-072209 (Spanish Ministry of Economy and Competitiveness). Ana B. Ruiz and José García-Nieto are recipient of a Post-Doctoral fellowship of “Captación de Talento para la Investigación” at Universidad de Málaga.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026