Nutcracker Optimization Algorithm (NOA)

版本 1.0.1 (8.1 MB) 作者: Reda Mohamed
Nutcracker Optimization Algorithm is a novel nature-inspired metaheuristic algorithm
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Nutcracker Optimization Algorithm (NOA) is a novel nature-inspired optimization algorithm that is inspired by two distinct behaviors of nutcrackers that occur at separate periods. The first behavior, which occurs during the summer and fall seasons, represents the nutcracker's search for seeds and subsequent storage in an appropriate cache. During the winter and spring seasons, another behavior based on the spatial memory strategy is regarded to search for the hidden caches marked at different angles using various objects or markers as reference points.
NOA is evaluated on test suites of CEC-2014, CEC-2017, and CEC-2020 and five real-world engineering design problems. NOA is compared with three classes of existing optimization algorithms: 1) SMA, GBO, EO, RUN, AVOA, RFO and GTO as recently-published algorithms, 2) SSA, WOA and GWO as highly-cited algorithms, and 3) AL-SHADE, L-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA as highly-performing optimizers and winners of CEC competition. NOA was ranked first among all methods and demonstrated superior results when compared to LSHADE-cnEpSin and LSHADE-SPACMA as the best-performing optimizers and the winners of CEC-2017.
Main paper: Abdel-Basset, M., Mohamed, R., Jameel, M., & Abouhawwash, M. (2023). Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems. Knowledge-Based Systems, 110248. DOI: https://doi.org/10.1016/j.knosys.2022.110248
The source code of this version is also available at https://github.com/redamohamed8/Function-evaluation-based-NOA-for-CEC2020

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Reda Mohamed (2024). Nutcracker Optimization Algorithm (NOA) (https://www.mathworks.com/matlabcentral/fileexchange/131059-nutcracker-optimization-algorithm-noa), MATLAB Central File Exchange. 检索时间: .

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版本 已发布 发行说明
1.0.1

The NOA's termination condition in this version relies on the maximum number of function evaluations, not the maximum number of iterations.
1.0.0