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.
|Title of host publication||Large-Scale Scientific Computing|
|Subtitle of host publication||9th International Conference, LSSC 2013, Sozopol, Bulgaria, June 3-7, 2013. Revised Selected Papers|
|Editors||Ivan Lirkov, Svetozar Margenov, Jerzy Waśniewski|
|Number of pages||8|
|Publication status||Published - 26 Jun 2014|
|Name||Lecture Notes in Computer Science|
|Publisher||Springer, Berlin, Heidelberg|