A Time Performance Evaluation of the Soma Asynchronous Parallel Distribution in Java and C

Abstract This paper compares two different implementations of the Self-Organizing Migrating Algorithm (SOMA), which is a highly effective tool of an evolutionary optimization that is aimed at the same set of problems as Genetic Algorithms. One implementation of algorithm was created in the C# framework and the second implementation in Java framework. Both implementations are the asynchronous parallel ‘All-to-One’ strategy of SOMA, which is used to equally distribute computation loads between several available processors/cores. The aim of our effort is to statistically evaluate the computation time efficiency of these two concurrent frameworks including dependence on the number of threads. The obtained results are discussed in the conclusion.

[1]  Vladimír Vašek,et al.  Algorithms in the examination of the postural stability , 2011 .

[2]  Roman Senkerik,et al.  Discrete Self-Organising Migrating Algorithm for flow-shop scheduling with no-wait makespan , 2013, Math. Comput. Model..

[3]  F. Mora-Camino,et al.  Studies in Fuzziness and Soft Computing , 2011 .

[4]  Michal Pluhacek,et al.  Investigation on evolutionary predictive control of chemical reactor , 2015, J. Appl. Log..

[5]  Ivan Zelinka,et al.  SOMA—Self-organizing Migrating Algorithm , 2016 .

[6]  Bronislav Chramcov,et al.  A simulation approach to achieving more efficient production systems , .

[7]  Ivan Zelinka,et al.  Self-Organizing Migrating Algorithm , 2016 .

[8]  M. K. Luhandjula Studies in Fuzziness and Soft Computing , 2013 .

[9]  Erik Kral,et al.  Usage of peak functions in heat load modeling of district heating system , 2011 .

[10]  Milan Adamek,et al.  Single and double layer spiral planar inductors optimisation with the aid of self-organising migrating algorithm , 2011 .

[11]  Roman Senkerik,et al.  Synthesis of feedback controller for three selected chaotic systems by means of evolutionary techniques: Analytic programming , 2013, Math. Comput. Model..

[12]  Pavel Vařacha Innovative strategy of SOMA control parameter setting , 2011 .

[13]  Viliam Dolinay,et al.  Simulation model of heat distribution and consumption in municipal heating network , 2010 .

[14]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.