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EDOA: An Elastic Deformation Optimization Algorithm

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Abstract

In recent years, a large number of meta-heuristic algorithms have been proposed to efficiently solve various complex optimization problems in reality. Most of these algorithms are based on the intelligent behavior of swarms in the natural world. In this article, we take Hooke's law of elasticity and Newton's second law of motion as the information interaction tools and innovatively propose a new meta-heuristic algorithm that is based on the laws of physics, called the elastic deformation optimization algorithm (EDOA). A new parameter adaptive adjustment mechanism is designed in the EDOA to better explore and exploit the search space. At the same time, we compare the proposed EDOA with six well-known search algorithms and conduct simulation experiments on 23 classical benchmark functions and IEEE CEC 2020 benchmark functions respectively. We have further analyzed the experimental results, used two nonparametric statistical test methods, and drawn iterative curves of the algorithms to prove the powerful comprehensive performance of the proposed EDOA.

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Acknowledgements

The authors would like to thank many developers of the algorithms in this paper for their valuable discussions on intelligent optimization algorithms, and thank the Editor-In-Chief, Associate Editor, and reviewers for their contributions to the improvement of this paper.

Funding

This work was supported by the National Natural Science Foundation Project of China [grant number 62073330] and the Natural Science Foundation Project of Hunan Province of China [grant number 2019JJ20021].

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  1. College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China

    Qingtao Pan, Jun Tang & Songyang Lao

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Correspondence to Jun Tang.

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Pan, Q., Tang, J. & Lao, S. EDOA: An Elastic Deformation Optimization Algorithm. Appl Intell 52, 17580–17599 (2022). https://doi.org/10.1007/s10489-022-03471-x

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