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⛄ 内容介绍
随着近十年来物联网 (IoT) 应用的飞速发展，对其算法进行更优化的研究改进也在猛增。温室监控是物联网最需要的用途之一，因为它降低了监控维护成本和错误。自动温室监测有助于持续管理环境因素并降低能源成本，而不会出现人为错误。物联网系统返回的数据可以转移到回归任务，以分析输入和目标之间的关系及其相关系数。同时，可以将这些数据聚类到相似的组中，以使数据易于理解和操作。为了执行这两项任务，引入了一种新的仿生算法。拟议的蜂虎狩猎（BEH）算法，不仅可以与遗传算法 (GA) 等著名的进化算法竞争，而且与其他算法相比，返回更优化的成本。ThingSpeak 平台返回的实时数据，发送到拟议的仿生 BEH 算法，用于模糊回归和聚类分析任务，并与其他算法进行比较。结果显示这两项任务都有相当大的改进。
⛄ 部分代码
%% Bee-Eater Hunting Algorithm (BEH)
% https://ieeexplore.ieee.org/abstract/document/9953726
% DOI: 10.1109/SCIoT56583.2022.9953726
% Please cite below:
% Mousavi, Seyed Muhammad Hossein. "Introducing Bee-Eater Hunting Strategy Algorithm
% for IoT-Based Green House Monitoring and Analysis." 2022 Sixth International Conference
% on Smart Cities, Internet of Things and Applications (SCIoT). IEEE, 2022.
% -----------------------------------------------------------------------------------
clc;
clear;
close all;
warning ('off');
%-----------------------------------------
nVar = 10; % Number of Decision Variables
VarSize = [1 nVar]; % Decision Variables Matrix Size
VarMin = -5; % Decision Variables Lower Bound
VarMax = 5; % Decision Variables Upper Bound
MaxIt = 200; % Maximum Number of Iterations
nPop = 10; % Number of bee-eaters
DamageRate = 0.2; % Damage Rate
nbeeeater = round(DamageRate*nPop); % Number of Remained beeeaters
nNew = nPop-nbeeeater; % Number of New beeeaters
mu = linspace(1, 0, nPop); % Mutation Rates
pMutation = 0.1; % Mutation Probability
MUtwo = 1-mu; % Fight Mutation
PeakPower = 0.8; % BeeEater Peack power Rate
AdjustPower = 0.03*(VarMax-VarMin); % BeeEater Adjustment Power Rate
PYR= -0.2; % Pitch Yaw Roll Rate
%----------------------------------------
⛄ 运行结果
⛄ 参考文献
Mousavi, Seyed Muhammad Hossein. “Introducing Bee-Eater Hunting Strategy Algorithm for IoT-Based Green House Monitoring and Analysis.” 2022 Sixth International Conference on Smart Cities, Internet of Things and Applications (SCIoT), IEEE, 2022, doi:10.1109/sciot56583.2022.9953726.
