An improved algorithm exploiting the characteristics of a distance-based preference function to converge to preferred solutions

We address the problem of choosing the most preferred of a set of alternatives that are defined by multiple criteria. We assume that the decision maker’s preferences can be represented by a general class of weighted distance functions that can take a wide variety of forms. We exploit the characteris...

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Bibliographic Details
Published in:European journal of operational research
Main Authors: Karakaya, G., Köksalan, M.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2025
Subjects:
Exact algorithm
Interactive algorithm
Most preferred solution
Multiple objective programming
Exact algorithm
Interactive algorithm
Multiple objective programming
Most preferred solution
ISSN:0377-2217
Online Access:Get full text
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Summary:We address the problem of choosing the most preferred of a set of alternatives that are defined by multiple criteria. We assume that the decision maker’s preferences can be represented by a general class of weighted distance functions that can take a wide variety of forms. We exploit the characteristics of these functions and develop an interactive algorithm that guarantees to find the most preferred alternative of a decision maker whose preferences are consistent with a distance-based function. In contrast with a benchmark algorithm that uses similar preference functions, our algorithm moves through different distance functions effectively to converge to the best alternative quickly. Our experiments on a variety of three- and four-objective problems demonstrate that our algorithm performs well, far outperforming the benchmark algorithm in terms of the required preference information from the decision maker. •Addresses choice problems considering distance-based preference functions.•Exploits the characteristics of distance-based functions during the search process.•Moves through different distance-based functions effectively.•Converges to the most preferred solution quickly.•Outperforms a benchmark algorithm in required preference information.
ISSN:0377-2217
DOI:10.1016/j.ejor.2025.08.036