On the Randomization of Indices Selection for Differential Evolution

This research deals with the hybridization of two softcomputing fields, which are the chaos theory and evolutionary algorithms. This paper investigates the utilization of the two-dimensional discrete chaotic systems, which are Burgers and Lozi maps, as the chaotic pseudo random number generators (CPRNGs) embedded into the selected heuristics, which is differential evolution algorithm (DE). Through the utilization of either chaotic systems or identical identified pseudo random number distribution, it is possible to fully keep or remove the hidden complex chaotic dynamics from the generated pseudo random data series. Experiments are focused on the extended investigation, whether the different randomization types with different pseudo random numbers distribution or hidden complex chaotic dynamics providing the unique sequencing are more beneficial to the heuristic performance. This research utilizes set of 4 selected benchmark functions, and totally four different randomizations; further results are compared against canonical DE.

[1]  Michal Pluhacek,et al.  Preliminary Study on the Randomization and Sequencing for the Chaos Embedded Heuristic , 2015, AECIA.

[2]  Luigi Fortuna,et al.  Chaotic sequences to improve the performance of evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[3]  Magdalena Metlicka,et al.  Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems , 2015, Swarm Evol. Comput..

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

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

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

[7]  Michal Pluhacek,et al.  On the parameter settings for the chaotic dynamics embedded differential evolution , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

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

[9]  Michal Pluhacek,et al.  Chaos particle swarm optimization with Eensemble of chaotic systems , 2015, Swarm Evol. Comput..

[10]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[11]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

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

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

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

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

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

[17]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

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

[19]  Leandro dos Santos Coelho,et al.  A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization , 2014, Appl. Math. Comput..

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

[21]  Janez Brest,et al.  Self-adaptive control parameters' randomization frequency and propagations in differential evolution , 2015, Swarm Evol. Comput..

[22]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

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

[24]  Ivan Zelinka,et al.  A survey on evolutionary algorithms dynamics and its complexity - Mutual relations, past, present and future , 2015, Swarm Evol. Comput..

[25]  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.