Chaos Driven Particle Swarm Optimization with Basic Particle Performance Evaluation – An Initial Study

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8838)


In this paper, the novel concept of particle performance evaluation is introduced into the chaos driven particle swarm optimization algorithm (PSO). The discrete chaotic dissipative standard map is used here as a chaotic pseudo-random number generator (CPRNG). In the novel proposed particle performance evaluation method the contribution of each particle to the process of obtaining the global best solution is investigated periodically. As a reaction to the possible poor performance of a particular particle, its velocity calculation is thereafter altered. Through utilization of this approach the convergence speed and overall performance of PSO algorithm driven by CPRNG based on Dissipative map is improved. The proposed method is tested on the CEC13 benchmark set with two different dimension settings.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Chaotic Sequence Original Particle Swarm Optimization Comprehensive Learn Particle Swarm Optimizer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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