Evaluating the Performance of Adaptive GainingSharing Knowledge Based Algorithm on CEC 2020 Benchmark Problems

This paper introduces an enhancement of the recent developed Gaining Sharing Knowledge based algorithm, dubbed as GSK. This algorithm is an excellent example of a contemporary nature-based algorithm which is inspired from the human life behavior of gaining and sharing knowledge to solve the optimization task. GSK algorithm simulates the natural phenomena of human gaining and sharing knowledge using two main phases: junior and senior. A set of initial solutions are generated at the beginning of the search which are consideredjuniors. Later, the individuals are moving to senior stage by interacting with the environment and cooperating with other solutions during the search.The key idea in this work is to extend and improve the original GSK algorithm by proposing adaptive settings to the two important control parameters: knowledge factor and knowledge ratio. These two parameters are responsible to control junior and senior gaining and sharing phases between the solutions during the optimization loop. The algorithm is named AGSK and tested on the recent benchmark suite on bound constrained numerical optimization which consists of different challenging optimization problems with different dimensions. This benchmark is presented in IEEE-CEC2020 competition. When compared with other state-of-the-art algorithms including original GSK, AGSK shows superior performance.

[1]  Daniel A. Ashlock,et al.  Evolutionary computation for modeling and optimization , 2005 .

[2]  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).

[3]  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).

[4]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[5]  Ponnuthurai N. Suganthan,et al.  Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

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

[7]  Anas A. Hadi,et al.  Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm , 2019, International Journal of Machine Learning and Cybernetics.

[8]  Anas A. Hadi,et al.  Single-Objective Real-Parameter Optimization: Enhanced LSHADE-SPACMA Algorithm , 2020 .

[9]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[10]  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 .

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

[12]  Pradnya A. Vikhar,et al.  Evolutionary algorithms: A critical review and its future prospects , 2016, 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC).

[13]  Panos M. Pardalos,et al.  History of Optimization , 2009, Encyclopedia of Optimization.

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