【优化算法】帝国主义竞争优化算法（ICA）【含Matlab源码 1635期】
一、获取代码方式二、帝国主义竞争优化算法简介
帝国主义算法流程描述如下:
1)初始化帝国主义竞争算法的参数:Npop,Nimp2)随机生成Npop作为国家的人口数量。选择Nimp最好的国家作为帝国并根据他们的能力规定他们的殖民地数量;
3)如果终止条件未得到满足,则重复下列步骤;
4)内部位置交换;
5)帝国主义竞争;
6)淘汰弱小的帝国;
7)保留最终剩余的帝国,其适应度取值作为最优解。
流程图如图1所示。
三、部分源代码
close all
clc; clear
%% Problem Statement
ProblemParams.CostFuncName = 'BenchmarkFunction'; % You should state the name of your cost function here.
ProblemParams.CostFuncExtraParams = 6;
ProblemParams.NPar = 30; % Number of optimization variables of your objective function. "NPar" is the dimention of the optimization problem.
ProblemParams.VarMin = -6; % Lower limit of the optimization parameters. You can state the limit in two ways. 1) 2)
ProblemParams.VarMax = 6; % Lower limit of the optimization parameters. You can state the limit in two ways. 1) 2)
% Modifying the size of VarMin and VarMax to have a general form
if numel(ProblemParams.VarMin)==1
ProblemParams.VarMin=repmat(ProblemParams.VarMin,1,ProblemParams.NPar);
ProblemParams.VarMax=repmat(ProblemParams.VarMax,1,ProblemParams.NPar);
end
ProblemParams.SearchSpaceSize = ProblemParams.VarMax - ProblemParams.VarMin;
%% Algorithmic Parameter Setting
AlgorithmParams.NumOfCountries = 200; % Number of initial countries.
AlgorithmParams.NumOfInitialImperialists = 8; % Number of Initial Imperialists.
AlgorithmParams.NumOfAllColonies = AlgorithmParams.NumOfCountries - AlgorithmParams.NumOfInitialImperialists;
AlgorithmParams.NumOfDecades = 2000;
AlgorithmParams.RevolutionRate = 0.3; % Revolution is the process in which the socio-political characteristics of a country change suddenly.
AlgorithmParams.AssimilationCoefficient = 2; % In the original paper assimilation coefficient is shown by "beta".
AlgorithmParams.AssimilationAngleCoefficient = .5;% In the original paper assimilation angle coefficient is shown by "gama".
AlgorithmParams.Zeta = 0.02; % Total Cost of Empire = Cost of Imperialist + Zeta * mean(Cost of All Colonies);
AlgorithmParams.DampRatio = 0.99;
AlgorithmParams.StopIfJustOneEmpire = false; % Use "true" to stop the algorithm when just one empire is remaining. Use "false" to continue the algorithm.
AlgorithmParams.UnitingThreshold = 0.02; % The percent of Search Space Size, which enables the uniting process of two Empires.
zarib = 1.05; % **** Zarib is used to prevent the weakest impire to have a probability equal to zero
alpha = 0.1; % **** alpha is a number in the interval of but alpha<<1. alpha denotes the importance of mean minimum compare to the global mimimum.
%% Display Setting
DisplayParams.PlotEmpires = false; % "true" to plot. "false" to cancel ploting.
if DisplayParams.PlotEmpires
DisplayParams.EmpiresFigureHandle = figure('Name','Plot of Empires','NumberTitle','off');
DisplayParams.EmpiresAxisHandle = axes;
end
DisplayParams.PlotCost = true; % "true" to plot. "false"
if DisplayParams.PlotCost
DisplayParams.CostFigureHandle = figure('Name','Plot of Minimum and Mean Costs','NumberTitle','off');
DisplayParams.CostAxisHandle = axes;
end
ColorMatrix = [1 0 0; 0 1 0 ; 0 0 1 ; 1 1 0; 1 0 1 ; 0 1 1 ; 1 1 1 ;
0.5 0.5 0.5; 0 0.5 0.5; 0.5 0 0.5; 0.5 0.5 0; 0.5 0 0 ; 0 0.5 0 ; 0 0 0.5 ;
1 0.5 1; 0.1*; 0.2*; 0.3*; 0.4*; 0.5*; 0.6*];
DisplayParams.ColorMatrix = ;
DisplayParams.AxisMargin.Min = ProblemParams.VarMin;
DisplayParams.AxisMargin.Max = ProblemParams.VarMax;
%% Creation of Initial Empires
InitialCountries = GenerateNewCountry(AlgorithmParams.NumOfCountries , ProblemParams);
% Calculates the cost of each country. The less the cost is, the more is the power.
if isempty(ProblemParams.CostFuncExtraParams)
InitialCost = feval(ProblemParams.CostFuncName,InitialCountries);
else
InitialCost = feval(ProblemParams.CostFuncName,InitialCountries,ProblemParams.CostFuncExtraParams);
end
= sort(InitialCost); % Sort the cost in assending order. The best countries will be in higher places
InitialCountries = InitialCountries(SortInd,:); % Sort the population with respect to their cost.
Empires = CreateInitialEmpires(InitialCountries,InitialCost,AlgorithmParams, ProblemParams);
%% Main Loop
MinimumCost = repmat(nan,AlgorithmParams.NumOfDecades,1);
MeanCost = repmat(nan,AlgorithmParams.NumOfDecades,1);
if DisplayParams.PlotCost
axes(DisplayParams.CostAxisHandle);
if any(findall(0)==DisplayParams.CostFigureHandle)
h_MinCostPlot=plot(MinimumCost,'r','LineWidth',1.5,'YDataSource','MinimumCost');
hold on;
h_MeanCostPlot=plot(MeanCost,'k:','LineWidth',1.5,'YDataSource','MeanCost');
hold off;
pause(0.05);
end
end
for Decade = 1:AlgorithmParams.NumOfDecades
AlgorithmParams.RevolutionRate = AlgorithmParams.DampRatio * AlgorithmParams.RevolutionRate;
Remained = AlgorithmParams.NumOfDecades - Decade
for ii = 1:numel(Empires)
%% Assimilation;Movement of Colonies Toward Imperialists (Assimilation Policy)
Empires(ii) = AssimilateColonies(Empires(ii),AlgorithmParams,ProblemParams);
%% Revolution;A Sudden Change in the Socio-Political Characteristics
Empires(ii) = RevolveColonies(Empires(ii),AlgorithmParams,ProblemParams);
%% New Cost Evaluation
if isempty(ProblemParams.CostFuncExtraParams)
Empires(ii).ColoniesCost = feval(ProblemParams.CostFuncName,Empires(ii).ColoniesPosition);
else
Empires(ii).ColoniesCost = feval(ProblemParams.CostFuncName,Empires(ii).ColoniesPosition,ProblemParams.CostFuncExtraParams);
end
%% Empire Possession(****** Power Possession, Empire Possession)
Empires(ii) = PossesEmpire(Empires(ii));
%% Computation of Total Cost for Empires
Empires(ii).TotalCost = Empires(ii).ImperialistCost + AlgorithmParams.Zeta * mean(Empires(ii).ColoniesCost);
end
%% Uniting Similiar Empires
Empires = UniteSimilarEmpires(Empires,AlgorithmParams,ProblemParams);
%% Imperialistic Competition
Empires = ImperialisticCompetition(Empires);
if numel(Empires) == 1 && AlgorithmParams.StopIfJustOneEmpire
break
end
%% Displaying the Results
DisplayEmpires(Empires,AlgorithmParams,ProblemParams,DisplayParams);
ImerialistCosts = ;
MinimumCost(Decade) = min(ImerialistCosts);
MeanCost(Decade) = mean(ImerialistCosts);
if DisplayParams.PlotCost
refreshdata(h_MinCostPlot);
refreshdata(h_MeanCostPlot);
drawnow;
pause(0.01);
end
四、运行结果
五、matlab版本及参考文献
1 matlab版本
2014a
2 参考文献
包子阳,余继周,杨杉.智能优化算法及其MATLAB实例（第2版）.电子工业出版社，2016.
张岩,吴水根.MATLAB优化算法源代码.清华大学出版社，2017.
陈志楚,李聪,张超勇.基于帝国主义竞争算法的切削参数优化.制造业自动化. 2012,34(24)
https://blog.51cto.com/u_15324424/4869400
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