A neighborhood-assisted evolutionary algorithm for multimodal multi-objective optimization-Reference-Cited by-同舟云学术

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A neighborhood-assisted evolutionary algorithm for multimodal multi-objective optimization

Published:2024-04-12 Issue:2 Volume:16 Page:139-158
ISSN:1865-9284
Container-title:Memetic Computing
language:en
Short-container-title:Memetic Comp.

Author:

Zhang Weiwei,Li Jiaqiang,Li Guoqing,Zhang Weizheng

Funder

Key research and development and promotion special project of Henan province

the Fundamental Research Funds for the Provincial Universities of Zhejiang

Zhejiang Provincial Natural Science Foundation of China under Grant

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12293-024-00410-w.pdf

Reference58 articles.

1. Jiang E, Wang L, Wang J (2021) Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks. Tsinghua Sci Technol 26(5):646–663

2. Yue CT, Liang JJ, Qu BY, Yu KJ, Song H (2019) Multimodal multiobjective optimization in feature selection. IEEE Congr Evol Comput (CEC) 2019:302–309

3. Moon T, Choi HW, Tzou N, Chatterjee A (2015) Wideband sparse signal acquisition with dual-rate time-interleaved undersampling hardware and multicoset signal reconstruction algorithms. IEEE Trans Signal Process 63(24):6486–6497

4. Zhan ZH, Shi L, Tan KC, Zhang J (2022) A survey on evolutionary computation for complex continuous optimization. Artif Intell Rev 55(1):59–110

5. Zhao H, Zhan ZH, Lin Y, Chen X, Luo XN, Zhang J, Kwong S, Zhang J (2019) Local binary pattern-based adaptive differential evolution for multimodal optimization problems. IEEE Trans Cybern 50(7):3343–3357

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