Global optimum-based search differential evolution

In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution ( DE ) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum, which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.

[1]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[2]  Dario Floreano,et al.  Memetic Viability Evolution for Constrained Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[3]  Ville Tirronen,et al.  Super-fit control adaptation in memetic differential evolution frameworks , 2009, Soft Comput..

[4]  Ville Tirronen,et al.  Scale factor local search in differential evolution , 2009, Memetic Comput..

[5]  Muhammad Khurram Khan,et al.  An effective memetic differential evolution algorithm based on chaotic local search , 2011, Inf. Sci..

[6]  Shuaiqun Wang,et al.  A Hybrid Discrete Imperialist Competition Algorithm for Fuzzy Job-Shop Scheduling Problems , 2016, IEEE Access.

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

[8]  Jiujun Cheng,et al.  Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[9]  Yang Yu,et al.  Multiple Chaos Embedded Gravitational Search Algorithm , 2017, IEICE Trans. Inf. Syst..

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

[11]  Jiujun Cheng,et al.  Ant colony optimization with clustering for solving the dynamic location routing problem , 2016, Appl. Math. Comput..

[12]  Kay Chen Tan,et al.  A Multi-Facet Survey on Memetic Computation , 2011, IEEE Transactions on Evolutionary Computation.

[13]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

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

[15]  David E. Goldberg,et al.  Optimizing Global-Local Search Hybrids , 1999, GECCO.

[16]  Natalio Krasnogor,et al.  Studies on the theory and design space of memetic algorithms , 2002 .

[17]  Graham Kendall,et al.  A Hyperheuristic Approach to Scheduling a Sales Summit , 2000, PATAT.

[18]  Jiujun Cheng,et al.  Connectivity Modeling and Analysis for Internet of Vehicles in Urban Road Scene , 2018, IEEE Access.

[19]  Shangce Gao,et al.  AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction , 2018, Knowl. Based Syst..

[20]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.

[21]  Qingtian Zeng,et al.  A Two-Layered Framework for the Discovery of Software Behavior: A Case Study , 2018, IEICE Trans. Inf. Syst..

[22]  M. J. D. Powell,et al.  An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..

[23]  Hitoshi Iba,et al.  Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.

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

[25]  Francisco Herrera,et al.  A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests , 2007, Expert Syst. Appl..

[26]  Ivor W. Tsang,et al.  Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP , 2015, IEEE Transactions on Evolutionary Computation.

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

[28]  Weiwei Zhang,et al.  Cooperative Differential Evolution With Multiple Populations for Multiobjective Optimization , 2016, IEEE Transactions on Cybernetics.

[29]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  J. E. Smith,et al.  Co-evolving memetic algorithms: a learning approach to robust scalable optimisation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[31]  Yan Wang,et al.  Gravitational search algorithm combined with chaos for unconstrained numerical optimization , 2014, Appl. Math. Comput..

[32]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[33]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[34]  Jiujun Cheng,et al.  Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[35]  MengChu Zhou,et al.  Routing in Internet of Vehicles: A Review , 2015, IEEE Transactions on Intelligent Transportation Systems.

[36]  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..

[37]  Pablo Moscato,et al.  A Gentle Introduction to Memetic Algorithms , 2003, Handbook of Metaheuristics.

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

[39]  Jim Smith,et al.  Co-evolving Memetic Algorithms: Initial Investigations , 2002, PPSN.

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

[41]  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 .

[42]  John Yearwood,et al.  Heterogeneous Cooperative Co-Evolution Memetic Differential Evolution Algorithm for Big Data Optimization Problems , 2017, IEEE Transactions on Evolutionary Computation.

[43]  Jason Sheng-Hong Tsai,et al.  Improving Differential Evolution With a Successful-Parent-Selecting Framework , 2015, IEEE Transactions on Evolutionary Computation.

[44]  Hitoshi Iba,et al.  Enhancing differential evolution performance with local search for high dimensional function optimization , 2005, GECCO '05.

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

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

[47]  Carlos García-Martínez,et al.  Memetic Algorithms for Continuous Optimisation Based on Local Search Chains , 2010, Evolutionary Computation.

[48]  Jiujun Cheng,et al.  Understanding differential evolution: A Poisson law derived from population interaction network , 2017, J. Comput. Sci..