An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems

An effective and efficient self-adaptation framework is proposed to improve the performance of the L-SHADE algorithm by providing successful alternative adaptation for the selection of control parameters. The proposed algorithm, namely LSHADE-EpSin, uses a new ensemble sinusoidal approach to automatically adapt the values of the scaling factor of the Differential Evolution algorithm. This ensemble approach consists of a mixture of two sinusoidal formulas: A non-Adaptive Sinusoidal Decreasing Adjustment and an adaptive History-based Sinusoidal Increasing Adjustment. The objective of this sinusoidal ensemble approach is to find an effective balance between the exploitation of the already found best solutions, and the exploration of non-visited regions. A local search method based on Gaussian Walks is used at later generations to increase the exploitation ability of LSHADE-EpSin. The proposed algorithm is tested on the IEEE CEC2014 problems used in the Special Session and Competitions on Real-Parameter Single Objective Optimization of the IEEE CEC2016. The results statistically affirm the efficiency and robustness of the proposed approach to obtain better results compared to L-SHADE algorithm and other state-of-the-art algorithms.

[1]  Arthur C. Sanderson,et al.  An approximate gaussian model of Differential Evolution with spherical fitness functions , 2007, 2007 IEEE Congress on Evolutionary Computation.

[2]  P. N. Suganthan,et al.  Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization , 2015, Appl. Soft Comput..

[3]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[4]  Ponnuthurai N. Suganthan,et al.  Self-adaptive differential evolution with multi-trajectory search for large-scale optimization , 2011, Soft Comput..

[5]  Francisco Herrera,et al.  Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems , 2011, Soft Comput..

[6]  Arthur C. Sanderson,et al.  Minimal representation multisensor fusion using differential evolution , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Ponnuthurai N. Suganthan,et al.  Modified differential evolution with local search algorithm for real world optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[8]  Ponnuthurai N. Suganthan,et al.  Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies , 2010, SEMCCO.

[9]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

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

[11]  Jason Sheng-Hong Tsai,et al.  A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

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

[14]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[15]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[16]  Janez Brest,et al.  Population size reduction for the differential evolution algorithm , 2008, Applied Intelligence.

[17]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[18]  Robert G. Reynolds,et al.  A differential evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[19]  Amer Draa,et al.  A sinusoidal differential evolution algorithm for numerical optimisation , 2015, Appl. Soft Comput..

[20]  Jafar Albadarneh,et al.  Cluster-based differential evolution with heterogeneous influence for numerical optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[21]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[22]  Antonio LaTorre,et al.  A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test , 2011, Soft Comput..