BFPA: Butterfly Strategy Flower Pollination Algorithm

Lei, Mengyi; Luo, Qifang; Zhou, Yongquan; Tang, Chengmei; Gao, Yuanyuan

doi:10.1007/978-3-030-26763-6_71

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…However, empirical studies revealed its enhanced optimization capacity in solving the Electric Load Dispatch (ELD) problem in power systems, outperforming traditional methods like PSO, GA, and the Chaos Optimization Algorithm (COA) . Further advancing this field, Jian et al introduced the Beetle Antennae Particle Swarm Optimization (BAPSO) (Lei et al 2019), tailored for resource coordination and scheduling challenges in edge computing. This evolved algorithm scales beetle individuals to a collective entity, integrating second-order oscillation and dynamic factors to augment the group mechanism.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

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“…Secondly, the two intermediate quantities are multiplied as follows: 20 h5_8E * 20 h4_71 = 20 h18_ABAE; (10) where * denotes the operation of multiplication. The result in (10) is an intermediate one in the overall computation.…”

Section: Fixed-point Arithmeticmentioning

confidence: 99%

“…Moreover, BAS plays an important role in algorithm optimization. Lei et al [10] designed an improved pollen algorithm with better performance in some areas based on BAS. Qi et al [22] used BAS for parameter optimization of Qubit neural tree networks.…”

Section: Introductionmentioning

confidence: 99%

A Hardware Descriptive Approach to Beetle Antennae Search

Yue

Jiang

et al. 2020

IEEE Access

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Beetle antennae search (BAS) is a newly developed meta-heuristic algorithm which is effectively used for optimizing objective functions of complex forms or even unknown forms. The common practice for implementing meta-heuristic algorithms including the BAS largely relies on programming in a high-level language and executing the code on a computer platform. However, the high-level implementation of the BAS algorithm hinders it from being used in an embedding system, where real-time operations are normally required. To address this limitation, we present an approach to implementing the BAS algorithm on a field-programmable gate array (FPGA). Specifically, we program the BAS function in the Verilog hardware description language (HDL), which provides a tractable vehicle for implementing the BAS algorithm at the gate level on the FPGA chip. We simulate our Verilog HDL based BAS module with the Modelsim platform. Simulation results validate the feasibility of our proposed Verilog HDL implementation of the BAS. Additionally, we implement the BAS model on the Zynq XC7Z010 platform, with 132.5 µ s latency for model implementation. INDEX TERMS FPGA, beetle antennae search, Verilog HDL, optimization algorithm.

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BFPA: Butterfly Strategy Flower Pollination Algorithm

Cited by 2 publications

References 8 publications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

A Hardware Descriptive Approach to Beetle Antennae Search

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