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Optimistic Variants of Single-Objective Bilevel Optimization for Evolutionary Algorithms

Sharma, Anuraganand (2020) Optimistic Variants of Single-Objective Bilevel Optimization for Evolutionary Algorithms. International Journal of Computational Intelligence and Applications, 19 . pp. 2050020-1. ISSN 1469-0268

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    Abstract

    Single-objective bilevel optimization is a specialized form of constraint optimization problems where one of the constraints is an optimization problem itself. These problems are typically non-convex and strongly NP-Hard. Recently, there has been an increased interest from the evolutionary computation community to model bilevel problems due to its applicability in real-world applications for decision-making problems. In this work, a partial nested evolutionary approach with a local heuristic search has been proposed to solve the benchmark problems and have outstanding results. This approach relies on the concept of intermarriage-crossover in search of feasible regions by exploiting information from the constraints. A new variant has also been proposed to the commonly used convergence approaches, i.e., optimistic and pessimistic. It is called an extreme optimistic approach. The experimental results demonstrate the algorithm converges differently to known optimum solutions with the optimistic variants. Optimistic approach also outperforms pessimistic approach. Comparative statistical analysis of our approach with other recently published partial to complete evolutionary approaches demonstrates very competitive results.

    Item Type: Journal Article
    Additional Information: Publisher: Imperial College Press
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Anuraganand Sharma
    Date Deposited: 24 Aug 2020 14:58
    Last Modified: 24 Aug 2020 15:43
    URI: http://repository.usp.ac.fj/id/eprint/12314
    UNSPECIFIED

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