Abstract
This paper proposes an improved hydrologic cycle optimization algorithm (IHCO) for solving real-world constrained engineering optimization problems. In the improved algorithm, a new flow strategy is carried out by utilizing the empirical knowledge of the population. Meanwhile, in order to balance exploration and exploitation, evaporation and precipitation operator in basic hydrologic cycle optimization is redesigned and an adaptive Gaussian mutation method is introduced. The standard deviation of the Gaussian distribution decreases linearly as the algorithm proceeds. Compared with several metaheuristic algorithms, the superiority of IHCO is validated through thirteen engineering optimization problems. The experimental results demonstrate that IHCO outperforms the basic algorithm, and it has a satisfactory capability to enhance performance.
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Acknowledgement
The study is supported by The National Natural Science Foundation of China (Nos. 71971143, 62103286), Major Project of Natural Science Foundation of China (No. 71790615), Integrated Project of Natural Science Foundation of China (No. 91846301), Social Science Youth Foundation of Ministry of Education of China (No. 21YJC630181), Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau (No. 2019KZDXM030), Natural Science Foundation of Guangdong Province (Nos.2020A1515010749, 2020A1515010752), Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515110401), Natural Science Foundation of Shenzhen City (No. JCYJ20190808145011259), and Guangdong Province Innovation Team (No. 2021WCXTD002).
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Qiu, H., Xue, B., Niu, B., Zhou, T., Lu, J. (2022). An Improved Hydrologic Cycle Optimization Algorithm for Solving Engineering Optimization Problems. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_10
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