🍁🥬🕒摘要🕒🥬🍁
蝙蝠使用回声定位技术检测猎物、避开障碍物以及在黑暗的环境中找到栖息地。其可以发出非常响亮的脉冲并听取从周围物体反弹回来的回声，根据回声到双耳的不同时间与强度判断物体所在的方向和位置；还可以根据目标猎物或者障碍物的特征发出不同性质的脉冲。
大多数蝙蝠使用恒定频率信号进行回声定位，信号的大小取决于目标猎物。蝙蝠发出的脉冲持续时间很短，一般在8~10 ms之间，其频率通常在25~150 kHz的范围内。正常飞行的过程中，蝙蝠每秒发射10~20个脉冲；而在寻找猎物的过程中，尤其在靠近猎物飞行时，每秒可以发射约200个脉冲。
蝙蝠算法（Bat Algorithm，BA）是受蝙蝠回声定位捕食行为启发，提出的一种基于迭代优化技术的新型群智能优化算法。该算法自2010年由Yang教授提出以来，因其具有模型简单、收敛速度快、参数少等优点 ，已在工程优化 、模型识别等问题中得到较好的应用，很快得到了国内外学者的广泛关注，成为智能优化算法领域新的研究热点。
✨🔎⚡运行结果⚡🔎✨
💂♨️👨🎓Matlab代码👨🎓♨️💂
clc; % clear any work or data in the command window clear all; % clear all varriable values before use close all; % close all open figures doc_name = 'ED_result.doc'; plot_Fcost = 'FuelCostCurve.png'; plot_Iterr = 'ItterationsCurve.png'; plot_Ploss = 'PowerLossCurve.png'; bar_Ploss = 'PowerLossChart.png'; bar_Fcost = 'FuelCostBar.png'; hvdc_Losses = 'HVDC_loses.png'; transmission_modes = ["HVAC","HVDC"]; source = ["6thermal","4thermal","2wind"]; % prelocating matrices that change in length [power_loss,F_cost,iterrations,sw_loss,cond_loss,tl_loss,F_cost_inst] = deal(zeros); demand = [120 150 180 210 240 270 300 330 360 390 420]; % load demands load_demand_values = numel(demand); % numel counts the elements of matrix print = fopen(doc_name,'w+'); % variables available to all functions global fuel_coefficients B power_demand Pg_limits transmission_type ... Cond_loss SW_loss TL_loss convergence_time start_timing DRi URi ... n f_cost beta tao time instability inst_const % fuel_coefficients matrix having 5 columns of fuel cost coefficients fuel_coefficients = [0.00375 2.00 240 0 0; 0.01750 1.75 200 0 0; 0.06250 1.00 220 40 0.008; 0.00834 3.25 200 30 0.009; 0.02500 3.00 220 0 0; 0.02500 3.00 190 0 0]; generator_limits = [50 200;20 80;15 50;10 35;10 30;12 40]; %RAMP RATE CONSTRAINTS DRi= [85 22 15 16 9 16]; URi= [65 12 12 8 6 8]; beta = 1.75; tao = 2.85; time = 10; % instability time in seconds instability = false(); % set the first calculations to be without instability n = length(fuel_coefficients(:,1)); %Returns the length of the fuel_coefficients variable for type = 1:numel(transmission_modes)% looping through each mode transmission_type = transmission_modes(type); fprintf(print,strcat('ECONOMIC DISPATCH FOR _',transmission_type,... ' USING NOVEL BAT OPTIMIZATION ALGORITHM \n')); %% Step 1:finding the B matrix loss_coef = [0.000218 0.000103 0.000009 -0.000010 0.000002 0.000027 0.000103 0.000181 0.000004 -0.000015 0.000002 0.000030 0.000009 0.000004 0.000417 -0.000131 -0.000153 -0.000107 -0.000010 -0.000015 -0.000131 0.000221 0.000094 0.000050 0.000002 0.000002 -0.000153 0.000094 0.000243 -0.000000 0.000027 0.000030 -0.000107 0.000050 -0.000000 0.000358]; %% Step 2: getting power demand and setting incremental cost(lamda) for idx = 1:load_demand_values power_demand = demand(idx); disp(strcat('Computing dispatch for >',num2str(power_demand),... 'MW in >',transmission_type,', ',num2str(load_demand_values... -idx),' more values to go...')) disp('Working please wait ...') %% Step3: Deploying Novel Bat Algorithm (NBA) if (min(generator_limits(:,1)) <= power_demand)&&(power_demand <= sum(generator_limits(:,2))) % setting the parameters in the basic Novel Bat Algorithm (NBA) M = 1000; %number of iterations pop = 30; gamma = 0.9; alpha = 0.99; r0Max = 1; r0Min = 0; AMax = 2; AMin = 1; freqDMax = 1.5; freqDMin = 0; % setting the additional parameters in Novel Bat Algorithm (NBA) G = 10; probMax = 0.9; probMin = 0.6; thetaMax = 1; thetaMin = 0.5; wMax = 0.9; wMin = 0.5; CMax = 0.9; CMin = 0.1; if strcmp(transmission_type,'HVDC') %assigning a different B for HVDC B = 0.45*loss_coef; else B = loss_coef; end
📜📢🌈参考文献🌈📢📜
[1]姜晨. 面向云制造多目标优化资源调度结果的预测方法研究[D].浙江工业大学,2019.DOI:10.27463/d.cnki.gzgyu.2019.000567.
