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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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