Interactive co-evolutionary multiple objective optimization algorithms for finding consensus solutions for a group of Decision Makers

Tomczyk, Michał K.; Kadziński, Miłosz

doi:10.1016/j.ins.2022.10.064

Cited by 8 publications

(1 citation statement)

References 43 publications

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“…Two other papers deal with multi-objective combinatorial problems, considering multiple DMs that must reach consensus, and are based on reference points and interactive preference elicitation mechanisms. Tomczyk and Kadzinski (2022) is based on the co-evolution of two populations, one called "primary", whose role is to discover solutions relevant to the committee, and another called "support", which approximates the entire Pareto front, revealing a variety of tradeoffs between objectives. On the other hand, Cinalli et al (2020) is based on collective intelligence reference points obtained by the interaction and aggregation of multiple opinions, and incorporates online and interactive eduction of collective-based preferences in NSGA-II, SPEA2 and the multiobjective S-metric evolutionary selection algorithm (SMSEMOA).…”

Section: Recent Preference-based Approachesmentioning

confidence: 99%

Hierarchical clustering-based framework for a posteriori exploration of Pareto fronts: application on the bi-objective next release problem

Casanova

Schab

Prado

et al. 2023

Front. Comput. Sci.

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IntroductionWhen solving multi-objective combinatorial optimization problems using a search algorithm without a priori information, the result is a Pareto front. Selecting a solution from it is a laborious task if the number of solutions to be analyzed is large. This task would benefit from a systematic approach that facilitates the analysis, comparison and selection of a solution or a group of solutions based on the preferences of the decision makers. In the last decade, the research and development of algorithms for solving multi-objective combinatorial optimization problems has been growing steadily. In contrast, efforts in the a posteriori exploration of non-dominated solutions are still scarce.MethodsThis paper proposes an abstract framework based on hierarchical clustering in order to facilitate decision makers to explore such a Pareto front in search of a solution or a group of solutions according to their preferences. An extension of that abstract framework aimed at addressing the bi-objective Next Release Problem is presented, together with a Dashboard that implements that extension. Based on this implementation, two studies are conducted. The first is a usability study performed with a small group of experts. The second is a performance analysis based on computation time consumed by the clustering algorithm.ResultsThe results of the initial empirical usability study are promising and indicate directions for future improvements. The experts were able to correctly use the dashboard and properly interpret the visualizations in a very short time. In the same direction, the results of the performance comparison highlight the advantage of the hierarchical clustering-based approach in terms of response time.DiscussionBased on these excellent results, the extension of the framework to new problems is planned, as well as the implementation of new validity tests with expert decision makers using real-world data.

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Section: Recent Preference-based Approachesmentioning

confidence: 99%

Hierarchical clustering-based framework for a posteriori exploration of Pareto fronts: application on the bi-objective next release problem

Casanova

Schab

Prado

et al. 2023

Front. Comput. Sci.

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Group Decision Making in Multiobjective Optimization: A Systematic Literature Review

Pajasmaa,

Miettinen,

Silvennoinen

2024

Group Decis Negot

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Group decision making has been studied from several viewpoints and a variety of methods has been proposed. However, in the literature on solving multiobjective optimization problems, the main focus has been on supporting a single decision maker. We conducted a systematic literature review to examine and synthesize the state-of-the-art of multiobjective optimization methods developed for group decision making. We analyze group decision making methods of multiobjective optimization according to how preferences of several decision makers are incorporated into the solution process, how to select the most preferred solution for the group, different types of decision makers, types of groups and how the group is to operate during the solution process. In addition, we identify the key issues in the literature that are required to be considered in further method development to increase the methods’ applicability in solving real-world problems. Finally, we guide how to select a method for solving real-world multiobjective optimization problems with multiple decision makers and suggest future research directions.

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Evolutionary algorithms for solving single- and multiple-objective political redistricting problems: The case study of Poland

Tomczyk,

Kadziński

2024

Applied Soft Computing

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Interactive co-evolutionary multiple objective optimization algorithms for finding consensus solutions for a group of Decision Makers

Cited by 8 publications

References 43 publications

Hierarchical clustering-based framework for a posteriori exploration of Pareto fronts: application on the bi-objective next release problem

Hierarchical clustering-based framework for a posteriori exploration of Pareto fronts: application on the bi-objective next release problem

Group Decision Making in Multiobjective Optimization: A Systematic Literature Review

Evolutionary algorithms for solving single- and multiple-objective political redistricting problems: The case study of Poland

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