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.