Adaptively Calling Selection Based on Distance Sorting in CoBiDE

Differential Evolution is fit for solving continuous optimization problems. So far, the imbalance between exploration and exploitation in DE runs often leads to the failure to obtain good solutions. In this paper, we propose selection based on distance sorting. In such selection, the individual has the best fitness among parents and offspring is selected firstly. Then, the genotype distance from another individual to it, the distance in their chromosome structure, decides whether the former individual is selected. Under the control of a adaptive scheme proposed by us, we use it replace the original selection of the CoBiDE in runs from time to time. Experimental results show that, for many among the twenty-five CEC 2005 benchmark functions, which have the similar changing trend of diversity and fitness in runs, our adaptive scheme for calling selection based on distance sorting brings improvement on solutions.

[1]  Lu Liu,et al.  Enhancing differential evolution with interactive information , 2018, Soft Comput..

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

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

[4]  Shao Yong Zheng,et al.  Differential evolution powered by collective information , 2017, Inf. Sci..

[5]  Yonghong Chen,et al.  Cellular direction information based differential evolution for numerical optimization: an empirical study , 2015, Soft Computing.

[6]  Mengnan Tian,et al.  Differential evolution with improved individual-based parameter setting and selection strategy , 2017, Appl. Soft Comput..

[7]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[8]  Robert G. Reynolds,et al.  An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[9]  Athanasios V. Vasilakos,et al.  Differential Evolution With Event-Triggered Impulsive Control , 2015, IEEE Transactions on Cybernetics.

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

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

[12]  Xuefeng Yan,et al.  Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies , 2016, IEEE Transactions on Cybernetics.

[13]  Vasileios A. Tatsis,et al.  Differential Evolution with Grid-Based Parameter Adaptation , 2017, Soft Comput..

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

[15]  Liang Gao,et al.  An improved adaptive differential evolution algorithm for continuous optimization , 2016, Expert Syst. Appl..

[16]  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).

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

[18]  Harish Sharma,et al.  Hybrid Artificial Bee Colony algorithm with Differential Evolution , 2017, Appl. Soft Comput..

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

[20]  Robert G. Reynolds,et al.  CADE: A hybridization of Cultural Algorithm and Differential Evolution for numerical optimization , 2017, Inf. Sci..

[21]  Gang Liu,et al.  Self-adaptive differential evolution with global neighborhood search , 2017, Soft Comput..

[22]  Liang Gao,et al.  Adaptive Differential Evolution With Sorting Crossover Rate for Continuous Optimization Problems , 2017, IEEE Transactions on Cybernetics.

[23]  Long Li,et al.  Differential evolution based on covariance matrix learning and bimodal distribution parameter setting , 2014, Appl. Soft Comput..

[24]  C. Jiang,et al.  An adaptive differential evolution algorithm with an aging leader and challengers mechanism , 2017, Appl. Soft Comput..