Global Optimization with Derivative-free, Derivative-based and Evolutionary Algorithms

H. A. Bashir, R. S. Neville

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    Abstract

    This paper investigates global optimization methodsfrom the perspective of population-based and restarted pointbasedheuristics. We examine the performance of a standardevolutionary computation (EC) methodology, a derivative-basedsequential quadratic programming (SQP) algorithm and a novelderivative-free stochastic coordinate ascent (SCA) algorithm. Allmethods are analyzed by random sampling of the feasible searchspace. A comparison was made to evaluate the three algorithms,in the light of newly updated IEEE CEC2013 benchmarks, on aset of multimodal and composite test cases. Results revealed thatwhile the standard EC algorithm is generally more robust, onthe basis of convergence efficiency both the restarted SCA andSQP algorithms have shown remarkable performance on someof these benchmarks. The results further suggest that dependingon the nature of the problem landscape and dimensionality thethree algorithms, chosen from different optimization frameworks,perform complementary to each other.
    Original languageEnglish
    Title of host publicationThe 2014 International Conference on Systems, Man, and Cybernetics
    Place of PublicationUSA
    PublisherIEEE
    Pages100-105
    Number of pages6
    DOIs
    Publication statusPublished - Oct 2014
    EventThe 2014 International Conference on Systems, Man, and Cybernetics - San Diego, CA, USA
    Duration: 5 Oct 20148 Oct 2014

    Conference

    ConferenceThe 2014 International Conference on Systems, Man, and Cybernetics
    CitySan Diego, CA, USA
    Period5/10/148/10/14

    Keywords

    • Global optimization, Sequential quadratic programming, Derivative-free stochastic coordinate ascent , EC algorithm , IEEE CEC2013 benchmarks

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