Soft Computing

, Volume 18, Issue 4, pp 619–629 | Cite as

Behaviour of pseudo-random and chaotic sources of stochasticity in nature-inspired optimization methods

  • Pavel Krömer
  • Ivan Zelinka
  • Václav Snášel


Stochasticity, noisiness, and ergodicity are the key concepts behind many natural processes and its modeling is an important part of their implementation. There is a handful of soft-computing methods that are directly inspired by nature or stochastic natural processes. The implementation of such a nature-inspired optimization and search methods usually depends on streams of integer and floating point numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as arbitrary modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration) and environmental effects (small random changes in motion direction and velocity). Deterministic chaos is a well known mathematical concept that can be used to generate sequences of seemingly random real numbers within selected interval in a predictable and well controllable way. In the past, it has been used as a basis for various pseudo-random number generators (PRNGs) with interesting properties. This work provides an empirical comparison of the behavior of selected nature-inspired optimization algorithms using different PRNGs and chaotic systems as sources of stochasticity.


Pseudo-random number generators Deterministic chaos  Simulation Genetic algorithms Differential evolution  Particle swarm optimization 



This work was supported by the European Regional Development Fund in the IT4-Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), by the Bio-Inspired Methods: research, development and knowledge transfer project, Reg. No. CZ.1.07/2.3.00/20.0073, and by the Development of human resources in research and development of latest soft computing methods and their application in practice project, Reg. No. CZ.1.07/2.3.00/20.0072 funded by Operational Programme Education for Competitiveness, co-financed by ESF and state budget of the Czech Republic. The following grant is also acknowledged for the financial support provided for this research: Grant Agency of the Czech Republic-GACR P103/13/08195S. The work was also partially supported by Grants of SGS No. SP2013/70 and No. SP2013/114, VŠB-Technical University of Ostrava.


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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.IT4Innovations and Department of Computer ScienceVŠB-Technical University of OstravaOstrava PorubaCzech Republic

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