@inproceedings{3a5b57c8fb234bb5a174322c03816b4d,
title = "Maximum Dispersion, Maximum Concentration: Enhancing the Quality of MOP Solutions",
abstract = "Multi-objective optimization problems (MOPs) often require a trade-off between conflicting objectives, maximizing diversity and convergence in the objective space. This study presents an approach to improve the quality of MOP solutions by optimizing the dispersion in the decision space and the convergence in a specific region of the objective space. Our approach defines a Region of Interest (ROI) based on a cone representing the decision maker{\textquoteright}s preferences in the objective space, while enhancing the dispersion of solutions in the decision space using a uniformity measure. Combining solution concentration in the objective space with dispersion in the decision space intensifies the search for Pareto-optimal solutions while increasing solution diversity. When combined, these characteristics improve the quality of solutions and avoid the bias caused by clustering solutions in a specific region of the decision space. Preliminary experiments suggest that this method enhances multi-objective optimization by generating solutions that effectively balance dispersion and concentration, thereby mitigating bias in the decision space.",
keywords = "Decision space diversity, Evolutionary algorithms, Multi-objective optimization, Region of Interest",
author = "Gladston Moreira and Ivan Meneghini and Elizabeth Wanner",
note = "Copyright {\textcopyright} The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. This version of the article has been accepted for publication, after peer review and is subject to Springer Nature{\textquoteright}s AM terms of use [ https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms ] but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-032-15984-7\_11 ; 35th Brazilian Conference on Intelligent Systems, BRACIS 2025 ; Conference date: 29-09-2025 Through 02-10-2025",
year = "2026",
month = jan,
day = "30",
doi = "10.1007/978-3-032-15984-7\_11",
language = "English",
isbn = "9783032159830 (pbk)",
series = "Lecture Notes in Computer Science",
pages = "150--165",
editor = "\{de Freitas\}, Rosiane and Diego Furtado",
booktitle = "Intelligent Systems",
}