Matlab代码书写规范

文章目录

标识符命名规则
- 一般变量
- 常量、全局变量
- 矩阵、结构体、元胞变量
- 结构体属性构建及引用方式
- 函数
代码版式
- 文件表头
- 函数说明
- 代码主体
- - 空行

标识符命名规则

一般变量

变量命名多采用小驼峰命名方式，如下

var serverForYou

循环变量多采用i,j,ki,j,ki,j,k等前缀形式命名，应避少用非前缀的命名方式，避免与matlab虚数单位重复

for i_population=1:K

常量、全局变量

常量或者全局变量均采用字母大写形式，如

DECISION

矩阵、结构体、元胞变量

矩阵，结构体及元胞变量多采用各类型英文名+"_"+变量实际代表含义名称，如

cell_mother
struct_population
matrix_fish

结构体属性构建及引用方式

构建


% state: optimization state of one generation
state = struct(...
'currentGen', 1,...         % current generation number
'evaluateCount', 0,...      % number of objective function evaluation
'totalTime', 0,...          % total time from the beginning
'firstFrontCount', 0,...    % individual number of first front
'frontCount', 0,...         % number of front
'avgEvalTime', 0 ...        % average evaluation time of objective function (current generation)
);

引用

options.popSize = 100;                   % populaion size
options.maxGen  = 150;                  % max generation 若此数值小于listbox值max，则不会显示options.numObj = 2;                     % number of objectives
options.numVar = 2;                     % number of design variables
options.numCons = 2;                    % number of constraints

函数

函数命名多采用小写字母+下划线分割形式命名

non_dominant_sort()

get/set前缀多用来访问对象或者属性用
compute前缀多用来计算某些量的函数用，如

compute_objectives()

is前缀多用来判断

isbool()

代码版式

文件表头

样例

%___________________________________________________________________%
%  Multi-Objective Grey Wolf Optimizer (MOGWO)                      %
%  Source codes demo version 1.0                                    %
%                                                                   %
%  Developed in MATLAB R2011b(7.13)                                 %
%                                                                   %
%  Author and programmer: Seyedali Mirjalili                        %
%                                                                   %
%         e-Mail: ali.mirjalili@gmail.com                           %
%                 seyedali.mirjalili@griffithuni.edu.au             %
%                                                                   %
%       Homepage: http://www.alimirjalili.com                       %
%                                                                   %
%   Main paper:                                                     %
%                                                                   %
%    S. Mirjalili, S. Saremi, S. M. Mirjalili, L. Coelho,           %
%    Multi-objective grey wolf optimizer: A novel algorithm for     %
%    multi-criterion optimization, Expert Systems with Applications,%
%    in press, DOI: http://dx.doi.org/10.1016/j.eswa.2015.10.039    %       %
%                                                                   %
%___________________________________________________________________%

函数说明

样例1：

function [ 函数输出参数] = 函数名( 函数输入)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%此处为函数名，如果不是函数文件就取消此行
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%创建人：
%日期：
%修改人：
%日期：
%函数变量及功能的简单描述
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--函数主体

样例2：

function result = nsga2(opt, varargin)
% Function: result = nsga2(opt, varargin)
% Description: The main flowchart of of NSGA-II. Note:
%   All objectives must be minimization. If a objective is maximization, the
%   objective should be multipled by -1.
%
% Syntax:
%   result = nsga2(opt): 'opt' is generated by function nsgaopt().
%   result = nsga2(opt, param): 'param' can be any data type, it will be
%       pass to the objective function objfun().
%
%   Then ,the result structure can be pass to plotnsga to display the
%   population:  plotnsga(result);
%
% Parameters:
%   opt : A structure generated by funciton nsgaopt().
%   varargin : Additional parameter will be pass to the objective functions.
%       It can be any data type. For example, if you call: nsga2(opt, param),
%       then objfun would be called as objfun(x,param), in which, x is the
%       design variables vector.
% Return:
%   result : A structure contains optimization result.
%
%         LSSSSWC, NWPU
%   Revision: 1.2  Data: 2011-07-26
%*************************************************************************

样例3：

%NDSort - Do non-dominated sorting by efficient non-dominated sort.
%
%   FrontNo = NDSort(F,s) does non-dominated sorting on F, where F is the
%   matrix of objective values of a set of individuals, and s is the number
%   of individuals to be sorted at least. FrontNo(i) denotes the front
%   number of the i-th individual. The individuals have not been sorted are
%   assigned a front number of inf.
%
%   FrontNo = NDSort(F,C,s) does non-dominated sorting based on constrained
%   domination, where C is the matrix of constraint values of the
%   individuals. In this case, feasible solutions always dominate
%   infeasible solutions, and one infeasible solution dominates another
%   infeasible solution if the former has a smaller overall constraint
%   violation than the latter.
%
%   In particular, s = 1 indicates finding only the first non-dominated
%   front, s = size(F,1)/2 indicates sorting only half the population
%   (which is often used in the algorithm), and s = inf indicates sorting
%   the whole population.
%
%   [FrontNo,K] = NDSort(...) also returns the maximum front number besides
%   inf.
%
%   Example:
%       [FrontNo,MaxFNo] = NDSort(PopObj,1)
%       [FrontNo,MaxFNo] = NDSort(PopObj,PopCon,inf)%------------------------------- Reference --------------------------------
% [1] X. Zhang, Y. Tian, R. Cheng, and Y. Jin, An efficient approach to
% nondominated sorting for evolutionary multiobjective optimization, IEEE
% Transactions on Evolutionary Computation, 2015, 19(2): 201-213.
% [2] X. Zhang, Y. Tian, R. Cheng, and Y. Jin, A decision variable
% clustering based evolutionary algorithm for large-scale many-objective
% optimization, IEEE Transactions on Evolutionary Computation, 2018, 22(1):
% 97-112.
%------------------------------- Copyright --------------------------------
% Copyright (c) 2018-2019 BIMK Group. You are free to use the PlatEMO for
% research purposes. All publications which use this platform or any code
% in the platform should acknowledge the use of "PlatEMO" and reference "Ye
% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform
% for evolutionary multi-objective optimization [educational forum], IEEE
% Computational Intelligence Magazine, 2017, 12(4): 73-87".
%--------------------------------------------------------------------------

代码主体

空行

空行起着分割块或段落的作用，块（段落）之间一般留一个或两个空行。

空行使用规则：

逻辑块之间要留空行

%*************************************************************************
% 1. Initialization
%*************************************************************************
numObj = length( pop(1).obj );  % number of objectivesrefPoints = opt.refPoints;
refWeight = opt.refWeight;      % weight factor of objectives
if(isempty(refWeight))refWeight = ones(1, numObj);
end
epsilon = opt.refEpsilon;
numRefPoint = size(refPoints, 1);% Determine the normalized factors
bUseFrontMaxMin = false;    % bUseFrontMaxMin : If use the maximum and minimum value in the front as normalized factor.
if( strcmpi(opt.refUseNormDistance, 'ever') )% 1) Find possiable (not current population) maximum and minimum value%     of each objective.obj = vertcat(pop.obj);if( ~isfield(opt, 'refObjMax_tmp') )opt.refObjMax_tmp = max(obj);opt.refObjMin_tmp = min(obj);elseobjMax = max(obj);objMin = min(obj);for i = 1:numObjif(opt.refObjMax_tmp(i) < objMax(i))opt.refObjMax_tmp(i) = objMax(i);endif(opt.refObjMin_tmp(i) > objMin(i))opt.refObjMin_tmp(i) = objMin(i);endendclear objMax objMinendobjMaxMin = opt.refObjMax_tmp - opt.refObjMin_tmp;clear obj
elseif( strcmpi(opt.refUseNormDistance, 'front') )% 2) Do not use normalized Euclidean distance.bUseFrontMaxMin = true;
elseif( strcmpi(opt.refUseNormDistance, 'no') )% 3) Do not use normalized Euclidean distance.objMaxMin = ones(1,numObj);
else% 3) Errorerror('NSGA2:ParamError', ...'No support parameter: options.refUseNormDistance="%s", only "yes" or "no" are supported',...opt.refUseNormDistance);
end%*************************************************************************
% 2. Calculate preference distance pop(:).prefDistance
%*************************************************************************
for fid = 1:length(front)% Step1: Calculate the weighted Euclidean distance in each frontidxFront = front(fid).f;            % idxFront : index of individuals in current frontnumInd = length(idxFront);          % numInd : number of individuals in current frontpopFront = pop(idxFront);           % popFront : individuals in front fidobjFront = vertcat(popFront.obj);   % objFront : the whole objectives of all individualsif(bUseFrontMaxMin)objMaxMin = max(objFront) - min(objFront); % objMaxMin : the normalized factor in current frontend% normDistance : weighted normalized Euclidean distancenormDistance = calcWeightNormDistance(objFront, refPoints, objMaxMin, refWeight);% Step2: Assigned preference distanceprefDistanceMat = zeros(numInd, numRefPoint);for ipt = 1:numRefPoint[~,ix] = sort(normDistance(:, ipt));%升序排序，选择距离小的prefDistanceMat(ix, ipt) = 1:numInd;%排序位置endprefDistance = min(prefDistanceMat, [], 2); % 2表示计算每行min 对于参考点距离的rank位置，rank的值当作参考距离clear ix% Step3: Epsilon clearing strategyidxRemain = 1:numInd;           % idxRemain : index of individuals which were not processedwhile(~isempty(idxRemain))% 1. Select one individual from remainsobjRemain = objFront( idxRemain, :);selIdx = randi( [1,length(idxRemain)] );selObj = objRemain(selIdx, :);% 2. Calc normalized Euclidean distance% distanceToSel : normalized Euclidean distance to the selected pointsdistanceToSel = calcWeightNormDistance(objRemain, selObj, objMaxMin, refWeight);% 3. Process the individuals within a epsilon-neighborhoodidx = find( distanceToSel <= epsilon );     % idx : index in idxRemainif(length(idx) == 1)    % the only individual is the selected oneidxRemain(selIdx)=[];elsefor i=1:length(idx)if( idx(i)~=selIdx )idInIdxRemain = idx(i);     % idx is the index in idxRemain vectorid = idxRemain(idInIdxRemain);% *Increase the preference distance to discourage the individuals% to remain in the selection.prefDistance(id) = prefDistance(id) + round(numInd/2);endendidxRemain(idx) = [];endend% Save the preference distancefor i=1:numIndid = idxFront(i);pop(id).prefDistance = prefDistance(i);end

结构体定义之间要留空行

% pop : current population
% newpop : new population created by genetic algorithm operators
% combinepop = pop + newpop;
pop = repmat( struct(...'var', zeros(1,nVar), ...   % 变量值'obj', zeros(1,nObj), ...  %目标函数值'cons', zeros(1,nCons),...  % 'rank', 0,...'distance', 0,...'prefDistance', 0,...       % preference distance used in R-NSGA-II'nViol', 0,...   % The number of constraints 'violSum', 0),... % The sum of abs(constraints)[1,popsize]);  %struct 默认是行形式存在的，如果[2, popsize]则与数组相同% state: optimization state of one generation
state = struct(...
'currentGen', 1,...         % current generation number
'evaluateCount', 0,...      % number of objective function evaluation
'totalTime', 0,...          % total time from the beginning
'firstFrontCount', 0,...    % individual number of first front
'frontCount', 0,...         % number of front
'avgEvalTime', 0 ...        % average evaluation time of objective function (current generation)
);

函数定义体之间要留空行

函数体内，完整的控制结构和单独的语句块之间要留空行

GreyWolves=CreateEmptyParticle(GreyWolves_num);for i=1:GreyWolves_numGreyWolves(i).Velocity=0;GreyWolves(i).Position=zeros(1,nVar);for j=1:nVarGreyWolves(i).Position(1,j)=unifrnd(lb(j),ub(j),1);endGreyWolves(i).Cost=fobj(GreyWolves(i).Position')';GreyWolves(i).Best.Position=GreyWolves(i).Position;GreyWolves(i).Best.Cost=GreyWolves(i).Cost;
endGreyWolves=DetermineDomination(GreyWolves);

     % Eq.(3.4) in the paperc=2.*rand(1, nVar);% Eq.(3.1) in the paperD=abs(c.*Beta.Position-GreyWolves(i).Position);% Eq.(3.3) in the paperA=2.*a.*rand()-a;% Eq.(3.9) in the paperX2=Beta.Position-A.*abs(D);% Eq.(3.4) in the paperc=2.*rand(1, nVar);% Eq.(3.1) in the paperD=abs(c.*Alpha.Position-GreyWolves(i).Position);% Eq.(3.3) in the paperA=2.*a.*rand()-a;% Eq.(3.10) in the paperX3=Alpha.Position-A.*abs(D);

逻辑上密切相关的语句序列之间不要留空行

未完，待续！

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