Neighborhood Learning-Based Cuckoo Search Algorithm for Global Optimization
| العنوان: | Neighborhood Learning-Based Cuckoo Search Algorithm for Global Optimization |
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| المؤلفون: | Yan Xiong, Jiatang Cheng, Lieping Zhang |
| المصدر: | International Journal of Pattern Recognition and Artificial Intelligence. 36 |
| بيانات النشر: | World Scientific Pub Co Pte Ltd, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Artificial Intelligence, Computer Vision and Pattern Recognition, Software |
| الوصف: | This paper presents a new variant of cuckoo search (CS) algorithm named neighborhood learning-based CS (NLCS) to address global optimization problems. Specifically, in this modified version, each individual learns from the personal best solution rather than the best solution found so far in the entire population to discourage premature convergence. To further enhance the performance of CS on complex multimode problems, each individual is allowed to learn from different learning exemplars on different dimensions. Moreover, the exemplar individual is chosen from a predefined neighborhood to further maintain the population diversity. This scheme enables each individual to interact with the historical experience of its own or its neighbors, which is controlled by using a learning probability. Extensive comparative experiments are conducted on 39 benchmark functions and two application problems of neural network training. Comparison results indicate that the proposed NLCS algorithm exhibits competitive convergence performance. |
| تدمد: | 1793-6381 0218-0014 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::cd59028d8236760702f7aa2eeb3236bb https://doi.org/10.1142/s0218001422510065 |
| رقم الأكسشن: | edsair.doi...........cd59028d8236760702f7aa2eeb3236bb |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17936381 02180014 |
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