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26-04-2024

Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm

Authors: Wenya Zhang, Jian Zhao, Hao Liu, Liangping Tu

Published in: The Journal of Supercomputing | Issue 12/2024

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Abstract

The Cleaner Fish Optimization (CFO) algorithm is a new meta-heuristic optimization method inspired by the social behaviors of cleaner fish. It aims to address the limitations of traditional optimization techniques, particularly in handling nonlinear, high-dimensional problems. The CFO algorithm simulates the behaviors of cleaner fish, such as goal-seeking and gender transitions, to explore and exploit the search space effectively. The article provides a detailed modeling process, including population initialization, update modes, and a two-generation cycle strategy. Experimental results on 23 benchmark functions and CEC-2017 benchmark functions demonstrate the CFO's superior exploration and exploitation capabilities compared to other meta-heuristic algorithms. Additionally, the CFO algorithm is applied to four engineering design problems, showing its practical effectiveness in real-world applications. The article concludes by highlighting the CFO's potential for solving large-scale optimization problems and its future applications in multi-objective optimization.

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Metadata
Title
Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm
Authors
Wenya Zhang
Jian Zhao
Hao Liu
Liangping Tu
Publication date
26-04-2024
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 12/2024
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-024-06105-w

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