Abstract
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.
Supported in part by National Natural Science Fundation of China (No. 61773119), and in part by Guangdong Universities’ Special Projects in Key Fields of Natural Science (No. 2019KZDZX1005).
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Acknowledgements
This work was supported in part by National Natural Science Fundation of China (No. 61773119), and in part by Guangdong Universities’ Special Projects in Key Fields of Natural Science (No. 2019KZDZX1005).
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Ma, H., Zhang, J., Wei, W., Cheng, W., Liu, Q. (2023). A Modified Gaining-Sharing Knowledge Algorithm Based on Dual-Population and Multi-operators for Unconstrained Optimization. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_25
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