Preliminary Study on the Randomization and Sequencing for the Chaos Embedded Heuristic

This research deals with the hybridization of the two softcomputing fields, which are chaos theory and evolutionary computation. This paper investigates the utilization of the time-continuous chaotic system, which is UEDA oscillator, as the chaotic pseudo random number generator. (CPRNG). Repeated simulations were performed investigating the influence of the oscillator sampling time to the selected heuristic, which is differential evolution algorithm (DE). Through the utilization of time-continuous systems and with different sampling times from very small to bigger, it is possible to fully keep, suppress or remove the hidden complex chaotic dynamics from the generated data series. Experiments are focused on the preliminary investigation, whether the different randomization given by particular CPRNG or hidden complex chaotic dynamics providing the unique sequencing are beneficial to the heuristic performance. Initial experiments were performed on the selected test function in several dimension settings.

[1]  Michal Pluhacek,et al.  Chaos PSO algorithm driven alternately by two different chaotic maps - An initial study , 2013, 2013 IEEE Congress on Evolutionary Computation.

[2]  Roman Senkerik,et al.  Chaos driven evolutionary algorithms for the task of PID control , 2010, Comput. Math. Appl..

[3]  J. Sprott.  Chaos and time-series analysis , 2001 .

[4]  Giovanni Iacca,et al.  Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead , 2012, Journal of Computer Science and Technology.

[5]  Ville Tirronen,et al.  Recent advances in differential evolution: a survey and experimental analysis , 2010, Artificial Intelligence Review.

[6]  Mehmet Karaköse,et al.  Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection , 2010, Expert Syst. Appl..

[7]  Wei Liang,et al.  The chaos differential evolution optimization algorithm and its application to support vector regression machine , 2011, J. Softw..

[8]  Michal Pluhacek,et al.  On the behavior and performance of chaos driven PSO algorithm with inertia weight , 2013, Comput. Math. Appl..

[9]  Bharti,et al.  Energy variability and chaos in Ueda oscillator , 2011 .

[10]  Ville Tirronen,et al.  A study on scale factor in distributed differential evolution , 2011, Inf. Sci..

[11]  Magdalena Metlicka,et al.  Chaos-driven Discrete Artificial Bee Colony , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[12]  L. Coelho,et al.  A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch , 2009 .

[13]  Kenneth V. Price,et al.  An introduction to differential evolution , 1999 .

[14]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[15]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[16]  Giovanni Iacca,et al.  Disturbed Exploitation compact Differential Evolution for limited memory optimization problems , 2011, Inf. Sci..

[17]  Xin-She Yang,et al.  Firefly algorithm with chaos , 2013, Commun. Nonlinear Sci. Numer. Simul..

[18]  Binggang Cao,et al.  Self-Adaptive Chaos Differential Evolution , 2006, ICNC.