A simple two-phase differential evolution for improved global numerical optimization

In the evolutionary computing community, differential evolution (DE) is well appreciated as a simple yet versatile population-based, non-convex optimizer designed for continuous optimization problems. A simple two-phase DE algorithm is presented in this article, which aims to identify promising basins of attraction on a non-convex functional landscape in the first phase, and starting from those previously identified search regions, a success history-based switch parameter DE is employed to further fine tune the search process leading to the optima of the landscape. Our proposed framework has been validated on the well-known IEEE Congress on Evolutionary Computation (CEC) benchmark suites (CEC 2013, 2014 and 2017). Results of the proposed method are compared with corresponding CEC winners (SHADE for CEC 2013, L-SHADE for CEC 2014 and jSO for CEC 2017).

[1]  MengChu Zhou,et al.  Differential evolution algorithms under multi-population strategy , 2017, 2017 26th Wireless and Optical Communication Conference (WOCC).

[2]  Robert G. Reynolds,et al.  An Adaptive Multipopulation Differential Evolution With Dynamic Population Reduction , 2017, IEEE Transactions on Cybernetics.

[3]  Rong Wang,et al.  Robust 2DPCA With Non-greedy $\ell _{1}$ -Norm Maximization for Image Analysis , 2015, IEEE Transactions on Cybernetics.

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

[5]  Shu-Mei Guo,et al.  Enhancing Differential Evolution Utilizing Eigenvector-Based Crossover Operator , 2015, IEEE Transactions on Evolutionary Computation.

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

[7]  Jing J. Liang,et al.  Differential Evolution With Neighborhood Mutation for Multimodal Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[8]  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 .

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

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

[11]  Swagatam Das,et al.  A Switched Parameter Differential Evolution with Multi-donor Mutation and Annealing Based Local Search for Optimization of Lennard-Jones Atomic Clusters , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[12]  Laizhong Cui,et al.  Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations , 2016, Comput. Oper. Res..

[13]  Tao Zhou,et al.  Multiview Latent Space Learning With Feature Redundancy Minimization , 2020, IEEE Transactions on Cybernetics.

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

[15]  Lixin Tang,et al.  Differential Evolution With an Individual-Dependent Mechanism , 2015, IEEE Transactions on Evolutionary Computation.

[16]  Zexuan Zhu,et al.  Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism , 2018, Inf. Sci..

[17]  Sanyang Liu,et al.  A Dual-Population Differential Evolution With Coevolution for Constrained Optimization , 2015, IEEE Transactions on Cybernetics.

[18]  Ponnuthurai N. Suganthan,et al.  Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation , 2015, Swarm Evol. Comput..

[19]  Ali Wagdy Mohamed,et al.  Adaptive guided differential evolution algorithm with novel mutation for numerical optimization , 2017, International Journal of Machine Learning and Cybernetics.

[20]  Janez Brest,et al.  Single objective real-parameter optimization: Algorithm jSO , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[21]  Wei-jie Yu,et al.  Multi-population differential evolution with adaptive parameter control for global optimization , 2011, GECCO '11.

[22]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[23]  Swagatam Das,et al.  Reusing the Past Difference Vectors in Differential Evolution—A Simple But Significant Improvement , 2020, IEEE Transactions on Cybernetics.

[24]  Ponnuthurai N. Suganthan,et al.  An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  Sankha Subhra Mullick,et al.  A switched parameter differential evolution with optional blending crossover for scalable numerical optimization , 2017, Appl. Soft Comput..

[26]  Haifeng Li,et al.  Ensemble of differential evolution variants , 2018, Inf. Sci..

[27]  Chao Jing,et al.  An improved multi-population ensemble differential evolution , 2018, Neurocomputing.

[28]  Dimitris K. Tasoulis,et al.  Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators , 2011, IEEE Transactions on Evolutionary Computation.

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

[30]  Swagatam Das,et al.  Multimodal optimization by artificial weed colonies enhanced with localized group search optimizers , 2013, Appl. Soft Comput..

[31]  Anas A. Hadi,et al.  Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization , 2019, Swarm Evol. Comput..

[32]  Bijaya K. Panigrahi,et al.  A noise resilient Differential Evolution with improved parameter and strategy control , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[33]  Guohua Wu,et al.  Differential evolution with multi-population based ensemble of mutation strategies , 2016, Inf. Sci..

[34]  Anas A. Hadi,et al.  LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[35]  Meie Shen,et al.  A Differential Evolution Algorithm With Dual Populations for Solving Periodic Railway Timetable Scheduling Problem , 2013, IEEE Transactions on Evolutionary Computation.

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

[37]  Kay Chen Tan,et al.  Multiple Exponential Recombination for Differential Evolution , 2017, IEEE Transactions on Cybernetics.

[38]  Laizhong Cui,et al.  A novel hybrid differential evolution algorithm with modified CoDE and JADE , 2016, Appl. Soft Comput..

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

[40]  Ali Wagdy Mohamed,et al.  Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation , 2017, Soft Computing.

[41]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[42]  Gui-Jun Zhang,et al.  Differential Evolution With Underestimation-Based Multimutation Strategy , 2019, IEEE Transactions on Cybernetics.

[43]  Swagatam Das,et al.  A Modified Differential Evolution With Distance-based Selection for Continuous Optimization in Presence of Noise , 2017, IEEE Access.