K-means cluster interactive algorithm-based evolutionary approach for solving bilevel multi-objective programming problems

Solving bilevel multi-objective programming problems is one of the hardest tasks facing researchers in the optimization community. Bilevel multi-objective programming problems is an optimization problem consists of two interconnected hierarchical multi-objective programming problems: upper-level pro...

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Bibliographic Details
Published in:Alexandria engineering journal Vol. 61; no. 1; pp. 811 - 827
Main Authors: Abo-Elnaga, Y., Nasr, S.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2022
Elsevier
Subjects:
Bilevel multi-objective programming
Evolutionary algorithms
Genetic algorithm
K-means clustering
Pareto optimal solution
Pareto optimal solution
Evolutionary algorithms
Bilevel multi-objective programming
Genetic algorithm
K-means clustering
ISSN:1110-0168
Online Access:Get full text
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Summary:Solving bilevel multi-objective programming problems is one of the hardest tasks facing researchers in the optimization community. Bilevel multi-objective programming problems is an optimization problem consists of two interconnected hierarchical multi-objective programming problems: upper-level problem and lower-level problem. Difficulty in solving bilevel multi-objective programming problems is the need to solve lower-level multi-objective programming problem to know the feasible space of the upper-level problem. The proposed algorithm consists of two nested artificial multi-objective algorithms. One algorithm is for the upper-level problem and the other is for the lower-level problem. Also, the proposed algorithm is enriched with a k-means cluster scheme in two phases. The first phase is before starting two nested algorithms to help the algorithm to start with more appropriates solutions to the bi-level problem. The second phase is within the two nested algorithms to guide the algorithm to the most preferred solutions to the upper-level decision-maker. The performance of the proposed algorithm has been evaluated on different test problems including low dimension and high dimension test problems. The experimental results show that the proposed algorithm is a feasible and efficient method for solving the bilevel multi-objective programming problem.
ISSN:1110-0168
DOI:10.1016/j.aej.2021.04.098