Insight into Adaptive Differential Evolution Variants with Unconventional Randomization Schemes

The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports an analysis of population dynamics by linking three elements like distribution of the results, population diversity, and differences between strategies of Differential Evolution (DE). Experiments utilize two frequently studied self-adaptive DE versions, which are simpler jDE and SHADE, further an original DE variant for comparisons, and totally ten chaos-driven quasi-random schemes for the indices selection in the DE. All important performance characteristics and population diversity are recorded and analyzed for the CEC 2015 benchmark set in 30D.

[1]  Ahmet Bedri Özer,et al.  CIDE: Chaotically Initialized Differential Evolution , 2010, Expert Syst. Appl..

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

[3]  Dirk Sudholt,et al.  The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses , 2018, Theory of Evolutionary Computation.

[4]  Bilal Alatas,et al.  Chaotic bee colony algorithms for global numerical optimization , 2010, Expert Syst. Appl..

[5]  Michal Pluhacek,et al.  Distance based parameter adaptation for Success-History based Differential Evolution , 2019, Swarm Evol. Comput..

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

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

[8]  Michal Pluhacek,et al.  Population Diversity Analysis in Adaptive Differential Evolution Variants with Unconventional Randomization Schemes , 2019, ICAISC.

[9]  Ajith Abraham,et al.  Chaotic dynamic characteristics in swarm intelligence , 2007, Appl. Soft Comput..

[10]  Matjaz Perc,et al.  A review of chaos-based firefly algorithms: Perspectives and research challenges , 2015, Appl. Math. Comput..

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

[12]  Jingrui Zhang,et al.  A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling , 2015 .

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

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

[15]  Guohua Wu,et al.  Ensemble strategies for population-based optimization algorithms - A survey , 2019, Swarm Evol. Comput..

[16]  Xie Zhi,et al.  Particle Swarm Optimization Algorithm Based on Chaotic Series , 2006 .

[17]  Hadi Mokhtari,et al.  A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system , 2015, Expert Syst. Appl..

[18]  Michal Pluhacek,et al.  How Unconventional Chaotic Pseudo-Random Generators Influence Population Diversity in Differential Evolution , 2018, ICAISC.

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

[20]  Alex S. Fukunaga,et al.  Improving the search performance of SHADE using linear population size reduction , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[21]  Amir Hossein Gandomi,et al.  Chaotic cuckoo search , 2015, Soft Computing.

[22]  Swagatam Das,et al.  Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis , 2015 .

[23]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for CEC 2015 Special Session on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization , 2015 .

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

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

[26]  Dogan Corus,et al.  Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms , 2017, IEEE Transactions on Evolutionary Computation.

[27]  Michal Pluhacek,et al.  On the Population Diversity for the Chaotic Differential Evolution , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

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

[29]  Michal Pluhacek,et al.  Differential Evolution and Chaotic Series , 2018, 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP).

[30]  Mark Hoogendoorn,et al.  Parameter Control in Evolutionary Algorithms: Trends and Challenges , 2015, IEEE Transactions on Evolutionary Computation.

[31]  Ajith Abraham,et al.  Chaos and Swarm Intelligence , 2009, Intelligent Computing Based on Chaos.

[32]  Michal Pluhacek,et al.  Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

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

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