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Language: enSubject: Combinatorial optimization

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Now showing 1 - 3 of 3
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    Citation - Scopus: 21
    An Empirical Study About Search-Based Refactoring Using Alternative Multiple and Population-Based Search Techniques
    (2012) Koc,E.; Ersoy,N.; Andac,A.; Camlidere,Z.S.; Cereci,I.; Kilic,H.
    Automated maintenance of object-oriented software system designs via refactoring is a performance demanding combinatorial optimization problem. In this study, we made an empirical comparative study to see the performances of alternative search algorithms under a quality model defined by an aggregated software fitness metric. We handled 20 different refactoring actions that realize searches on design landscape defined by combination of 24 object-oriented software metrics. The investigated algorithms include random, steepest descent, multiple first descent, multiple steepest descent, simulated annealing and artificial bee colony searches. The study is realized by using a tool called A-CMA developed in Java that accepts bytecode compiled Java codes as its input. The empiricial study showed that multiple steepest descent and population-based artificial bee colony algorithms are two most suitable approaches for the efficient solution of the search based refactoring problem. © 2012 Springer-Verlag London Limited.
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    Conference Object
    Citation - WoS: 6
    Citation - Scopus: 6
    Comparing Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms for Solving the Set Covering Problem
    (Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Galleguillos, Cristian; Barraza, Jorge; Lizama, Sebastian; Munoz, Alexis; Paredes, Fernando
    The set covering problem is a classical model in the subject of combinatorial optimization for service allocation, that consists in finding a set of solutions for covering a range of needs at the lowest possible cost. In this paper, we report various approximate methods to solve this problem, such as Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms. We illustrate experimental results of these metaheuristics for solving a set of 65 non-unicost set covering problems from the Beasley's OR-Library.
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    Conference Object
    Citation - WoS: 17
    An Empirical Study About Search-Based Refactoring Using Alternative Multiple and Population-Based Search Techniques
    (Springer-verlag London Ltd, 2012) Koc, Ekin; Ersoy, Nur; Andac, Ali; Camlidere, Zelal Seda; Cereci, Ibrahim; Kilic, Hurevren
    Automated maintenance of object-oriented software system designs via refactoring is a performance demanding combinatorial optimization problem. In this study, we made an empirical comparative study to see the performances of alternative search algorithms under. a quality model defined by an aggregated software fitness metric. We handled 20 different refactoring actions that realize searches on design landscape defined by combination of 24 object-oriented software metrics. The investigated algorithms include random, steepest descent, multiple first descent, multiple steepest descent, simulated annealing and artificial bee colony searches. The study is realized by using a tool called A-CMA developed in Java that accepts bytecode compiled Java codes as its input. The empiricial study showed that multiple steepest descent and population-based artificial bee colony algorithms are two most suitable approaches for the efficient solution of the search based refactoring problem.