RBMO: Red-billed blue magpie optimizer

版本 1.0.1 (18.1 MB) 作者: Shengwei Fu
Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems
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更新时间 2024/6/19

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Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and engineering design problems are fundamental to the development of artificial intelligence. Traditional methods show limitations in dealing with these complex nonlinear models. To address these challenges, the swarm intelligence algorithm is introduced as a metaheuristic method and effectively implemented. However, existing technology exhibits drawbacks such as slow convergence speed, low precision, and poor robustness. In this paper, we propose a novel metaheuristic approach called the Red-billed Blue Magpie Optimizer (RBMO), inspired by the cooperative and efficient predation behaviors of red-billed blue magpies. The mathematical model of RBMO was established by simulating the searching, chasing, attacking prey, and food storage behaviors of the red-billed blue magpie. To demonstrate RBMO’s performance, we first conduct qualitative analyses through convergence behavior experiments. Next, RBMO’s numerical optimization capabilities are substantiated using CEC2014 (Dim = 10, 30, 50, and 100) and CEC2017 (Dim = 10, 30, 50, and 100) suites, consistently achieving the best Friedman mean rank. In UAV path planning applications (two-dimensional and three − dimensional), RBMO obtains preferable solutions, demonstrating its effectiveness in solving NP-hard problems. Additionally, in five engineering design problems, RBMO consistently yields the minimum cost, showcasing its advantage in practical problem-solving. We compare our experimental results with three categories of widely recognized algorithms: (1) advanced variants, (2) recently proposed algorithms, and (3) high-performance optimizers, including CEC winners.
% Red-billed Blue Magpie Optimizer (RBMO)
% Source codes demo version 1.0 using matlab2023a
% Author and programmer: Shengwei Fu e-Mail: shengwei_fu@163.com
% Paper : Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems
% Journal : Artificial Intelligence Review
% DOI : https://doi.org/10.1007/s10462-024-10716-3

引用格式

Shengwei Fu (2024). RBMO: Red-billed blue magpie optimizer (https://www.mathworks.com/matlabcentral/fileexchange/165136-rbmo-red-billed-blue-magpie-optimizer), MATLAB Central File Exchange. 检索时间: .

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RBMO/RBMO_CEC2014

RBMO/RBMO_CEC2017

版本 已发布 发行说明
1.0.1

Fixed CEC2014 errors.
1.0.0