Abstract
Intelligent optimization is a kind of global optimization algorithms based on simulating biological intelligent behaviors such as evolution and foraging. Currently, there are numerous intelligent optimization algorithms have been proposed based on a large mount of animals’ or plants’ behaviors. This phenomenon shows the prosperity of this field, but bring issues about these algorithms’ analysis and applications. We believe an extensive development stage has passed in the field of intelligent optimization, and more theoretical analysis and deep understanding about these algorithms become favorite. In this paper, we try to build a general framework for all population-based global optimization algorithms. This framework employs the idea of multilevel evolution, and therefore it can include not only the traditional bio-inspired evolution algorithms which often only evolute in a single level of search space, but also those population-based algorithms adopt data-driven strategies or cultural evolutions. By the help of the proposed framework, we can classify all population-based global optimization algorithms into three types, and improve the traditional algorithms. In this paper, this framework is then applied to the popular particle swarm optimization, and a modified particle swarm optimization with three-level of evolutions is proposed. Numerical results show that the modified algorithm improves the original one significantly.
Supported by Guangdong Universities’ Special Projects in Key Fields of Natural Science under Grant 2019KZDZX1005.
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This paper is supported by the Guangdong Universities’ Special Projects in Key Fields of Natural Science under Grant 2019KZDZX1005.
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Wang, C., Chen, C., Lun, Z., Ye, Z., Liu, Q. (2022). A General Framework for Intelligent Optimization Algorithms Based on Multilevel Evolutions. 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_2
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