A supercomputing method for large-scale optimization: a feedback biogeography-based optimization with steepest descent method-Reference-Cited by-同舟云学术

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A supercomputing method for large-scale optimization: a feedback biogeography-based optimization with steepest descent method

Published:2022-07-23 Issue:2 Volume:79 Page:1318-1373
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Zhang Ziyu,Gao Yuelin^ORCID,Guo Eryang

Funder

the Key Project of Ningxia Natural Science Foundation

the 2022 Graduate Innovation Project of North Minzu University

the National Natural Science Foundation of China under Grant

the Construction Project of First-class Subjects in Ningxia Higher Education

the Major Proprietary Funded Project of North Minzu University

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-022-04644-8.pdf

Reference74 articles.

1. Tan Z, Li K, Wang Y (2021) Differential evolution with adaptive mutation strategy based on fitness landscape analysis. Inf Sci 549:142–163. https://doi.org/10.1016/j.ins.2020.11.023

2. Cao Y, Zhang H, Li W et al (2018) Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Trans Evol Comput 23(4):718–731. https://doi.org/10.1109/TEVC.2018.2885075

3. Mohamed AW, Hadi AA, Fattouh AM et al (2017) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 145–152. https://doi.org/10.1109/CEC.2017.7969307

4. He L, Cao Y, Li W et al (2022) Optimization of energy-efficient open shop scheduling with an adaptive multi-objective differential evolution algorithm. Appl Soft Comput 2022:108459. https://doi.org/10.1016/j.asoc.2022.108459

5. Biedrzycki R (2017) A version of IPOP-CMA-ES algorithm with midpoint for CEC 2017 single objective bound constrained problems. In: 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1489–1494. https://doi.org/10.1109/CEC.2017.7969479

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1. Optimizing production efficiency in semiconductor enterprises by an improve and optimized biogeographical optimization algorithm based on three-layer coding;Intelligent Systems with Applications;2024-12

2. A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems;Mathematics;2024-01-29

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