A Modified Gaining-Sharing Knowledge Algorithm Based on Dual-Population and Multi-operators for Unconstrained Optimization.

Evolutionary Algorithms (EAs) have a great power to solve complex optimization problems. In recent years, Gaining-Sharing Knowledge algorithm (GSK) is one of the novel nature-inspired algorithms to solve real life optimization problems. This paper introduces a modified Gaining-Sharing Knowledge algorithm based on dual-population framework and multi-operators (mGSK-DPMO). Meanwhile, a hybrid parameter adaptive technique is implemented in order to enhance its performance. The numerical result shows that the presented algorithm (mGSK-DPMO) is found to be highly competitive and significantly superior to other state-of-the-art algorithms including CEC2020’s top 3 algorithms in CEC2020 benchmark problems.