Free Search in Multidimensional Space

    Research output: Chapter in Book/Report/Published conference proceedingChapter

    Abstract

    One of the challenges for modern search methods is resolving multidimensional tasks where optimization parameters are hundreds, thousands and more. Many evolutionary, swarm and adaptive methods, which perform well on numerical test with up to 10 dimensions are suffering insuperable stagnation when are applied to the same tests extended to 50, 100 and more dimensions. This article presents an original investigation on Free Search, Differential Evolution and Particle Swarm Optimization applied to multidimensional versions of several heterogeneous real-value numerical tests. The aim is to identify how dimensionality reflects on the search space complexity, in particular to evaluate relation between tasks’ dimensions’ number and corresponding iterations’ number required by used methods for reaching acceptable solution with non-zero probability. Experimental results are presented and analyzed.
    Original languageEnglish
    Title of host publicationLarge-Scale Scientific Computing
    Subtitle of host publication9th International Conference, LSSC 2013, Sozopol, Bulgaria, June 3-7, 2013. Revised Selected Papers
    EditorsIvan Lirkov, Svetozar Margenov, Jerzy Waśniewski
    Pages289-296
    Number of pages8
    Volume8353
    Edition1
    ISBN (Electronic)978-3-662-43880-0
    DOIs
    Publication statusPublished - 26 Jun 2014

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer, Berlin, Heidelberg
    Number1
    Volume8353

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  • Cite this

    Penev, K. (2014). Free Search in Multidimensional Space. In I. Lirkov, S. Margenov, & J. Waśniewski (Eds.), Large-Scale Scientific Computing: 9th International Conference, LSSC 2013, Sozopol, Bulgaria, June 3-7, 2013. Revised Selected Papers (1 ed., Vol. 8353, pp. 289-296). (Lecture Notes in Computer Science; Vol. 8353, No. 1). https://doi.org/10.1007/978-3-662-43880-0_32