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Flamingo Search Algorithm and Its Application to Path Planning Problem

Published: 25 February 2022 Publication History

Abstract

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://github.com/18280426650/FSA.

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Cited By

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  • (2023)Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical textMathematical Biosciences and Engineering10.3934/mbe.202324420:3(5268-5297)Online publication date: 2023
  • (2023)Feature Selection with a Binary Flamingo Search Algorithm and a Genetic AlgorithmMultimedia Tools and Applications10.1007/s11042-023-15467-x82:17(26679-26730)Online publication date: 15-May-2023
  • (2023)Modeling and optimization of hybrid renewable energy with storage system using flamingo swarm intelligence algorithmsEnergy Storage10.1002/est2.4705:7Online publication date: 30-Mar-2023

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        cover image ACM Other conferences
        AIPR '21: Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
        September 2021
        715 pages
        ISBN:9781450384087
        DOI:10.1145/3488933
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Publication History

        Published: 25 February 2022

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        Author Tags

        1. Constrained optimization
        2. Flamingo search algorithm
        3. Path planning problem
        4. Swarm intelligence optimization algorithm

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        Cited By

        View all
        • (2023)Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical textMathematical Biosciences and Engineering10.3934/mbe.202324420:3(5268-5297)Online publication date: 2023
        • (2023)Feature Selection with a Binary Flamingo Search Algorithm and a Genetic AlgorithmMultimedia Tools and Applications10.1007/s11042-023-15467-x82:17(26679-26730)Online publication date: 15-May-2023
        • (2023)Modeling and optimization of hybrid renewable energy with storage system using flamingo swarm intelligence algorithmsEnergy Storage10.1002/est2.4705:7Online publication date: 30-Mar-2023

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