目录介绍：

• 1、黑客帝国矩阵重启解析是什么？
• 2、有没有人有多目标人工蜂群算法的MATLAB代码。发我一份 不胜感激！！
• 3、人工蜂群算法的matlab的编程详细代码，最好有基于人工蜂群算法的人工神经网络的编程代码？

黑客帝国矩阵重启解析是什么？

黑客帝国矩阵重启解析：

在最后战役的60年后，重生的救世主尼奥忘了先前发生的事情回归平凡人身份，但随着遇见爱人崔妮蒂之后，尼奥也在黑客兔儿和年轻莫菲斯的带领下重返母体，面对幻觉寻找背后真相，再次为自己心爱的人而战。

结局中，就在崔尼蒂要出门的那一刻，瞬间回忆起来了，架构师启动了蜂群控制人们的思想，直接开打，两人手拉手直接释放冲击波，架构师缓慢了时间，拿枪准备杀死崔尼蒂。

史密斯突然起来把架构师揍了一顿，对尼奥说了一句我们暂时的合作结束了，而此时整个矩阵的人都成了蜂群，开始追杀尼奥和崔尼蒂，两人终于逃到楼顶，尼奥用气功引开了导 弹，两人手牵手跳下楼。

尼奥却不会飞，四周都是飞机，崔尼蒂拉住尼奥的手，对尼奥说了句bye，就要把尼奥丢到楼顶，但就在此时，两个人离开了矩阵，所有人都站在一起，没有人离去。

《黑客帝国：矩阵重启》的特色世界观  

从《猎杀星期一》、《混沌行走》、《阿丽塔：战斗天使》，到动画片《伊甸》与《烟囱小镇的普佩尔》，多年来观众都已经对反乌托邦电影非常熟悉。

但《黑客帝国》这部系列作品最特别且难以取代的地方，还是在于导演沃卓斯基姐妹用数字位所组成的“代码”建构出整部作品在“后末日”世界，大部分人类都遭到人工智慧给囚禁在虚拟空间的世界观设计。

其中这不仅让人对《黑客帝国》这有着独特概念的反乌托邦故事产生浓厚的兴趣，在整部作品以作为“救世主”的主角尼奥必须带领幸存人类反抗人工智慧的故事构架下。

导演也在利用片中枪战驳火与慢动作的打斗场面给观众带来娱乐爽度之余，透过众多剧情元素对宗教概念、哲学思想，还有人们的自由意志与世俗主义精神等各项议题进行深入的探讨。

有没有人有多目标人工蜂群算法的MATLAB代码。发我一份 不胜感激！！

;aid=11221pay=yes

里面有多个文件

其中之一

%/* ABC algorithm coded using MATLAB language */

%/* Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. */

%/* Referance Papers*/

%/*D. Karaboga, AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION,TECHNICAL REPORT-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department 2005.*/

%/*D. Karaboga, B. Basturk, A powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm, Journal of Global Optimization, Volume:39, Issue:3,pp:459-171, November 2007,ISSN:0925-5001 , doi: 10.1007/s10898-007-9149-x */

%/*D. Karaboga, B. Basturk, On The Performance Of Artificial Bee Colony (ABC) Algorithm, Applied Soft Computing,Volume 8, Issue 1, January 2008, Pages 687-697. */

%/*D. Karaboga, B. Akay, A Comparative Study of Artificial Bee Colony Algorithm, Applied Mathematics and Computation, 214, 108-132, 2009. */

%/*Copyright ?2009 Erciyes University, Intelligent Systems Research Group, The Dept. of Computer Engineering*/

%/*Contact:

%Dervis Karaboga (karaboga@erciyes.edu.tr )

%Bahriye Basturk Akay (bahriye@erciyes.edu.tr)

%*/

clear all

close all

clc

%/* Control Parameters of ABC algorithm*/

NP=20; %/* The number of colony size (employed bees+onlooker bees)*/

FoodNumber=NP/2; %/*The number of food sources equals the half of the colony size*/

limit=100; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/

maxCycle=2500; %/*The number of cycles for foraging {a stopping criteria}*/

%/* Problem specific variables*/

objfun='Sphere'; %cost function to be optimized

D=100; %/*The number of parameters of the problem to be optimized*/

ub=ones(1,D)*100; %/*lower bounds of the parameters. */

lb=ones(1,D)*(-100);%/*upper bound of the parameters.*/

runtime=1;%/*Algorithm can be run many times in order to see its robustness*/

%Foods [FoodNumber][D]; /*Foods is the population of food sources. Each row of Foods matrix is a vector holding D parameters to be optimized. The number of rows of Foods matrix equals to the FoodNumber*/

%ObjVal[FoodNumber]; /*f is a vector holding objective function values associated with food sources */

%Fitness[FoodNumber]; /*fitness is a vector holding fitness (quality) values associated with food sources*/

%trial[FoodNumber]; /*trial is a vector holding trial numbers through which solutions can not be improved*/

%prob[FoodNumber]; /*prob is a vector holding probabilities of food sources (solutions) to be chosen*/

%solution [D]; /*New solution (neighbour) produced by v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) j is a randomly chosen parameter and k is a randomlu chosen solution different from i*/

%ObjValSol; /*Objective function value of new solution*/

%FitnessSol; /*Fitness value of new solution*/

%neighbour, param2change; /*param2change corrresponds to j, neighbour corresponds to k in equation v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij})*/

%GlobalMin; /*Optimum solution obtained by ABC algorithm*/

%GlobalParams[D]; /*Parameters of the optimum solution*/

%GlobalMins[runtime]; /*GlobalMins holds the GlobalMin of each run in multiple runs*/

GlobalMins=zeros(1,runtime);

for r=1:runtime

% /*All food sources are initialized */

%/*Variables are initialized in the range [lb,ub]. If each parameter has different range, use arrays lb[j], ub[j] instead of lb and ub */

Range = repmat((ub-lb),[FoodNumber 1]);

Lower = repmat(lb, [FoodNumber 1]);

Foods = rand(FoodNumber,D) .* Range + Lower;

ObjVal=feval(objfun,Foods);

Fitness=calculateFitness(ObjVal);

%reset trial counters

trial=zeros(1,FoodNumber);

%/*The best food source is memorized*/

BestInd=find(ObjVal==min(ObjVal));

BestInd=BestInd(end);

GlobalMin=ObjVal(BestInd);

GlobalParams=Foods(BestInd,:);

iter=1;

while ((iter = maxCycle)),

%%%%%%%%% EMPLOYED BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%

for i=1:(FoodNumber)

%/*The parameter to be changed is determined randomly*/

Param2Change=fix(rand*D)+1;

%/*A randomly chosen solution is used in producing a mutant solution of the solution i*/

neighbour=fix(rand*(FoodNumber))+1;

%/*Randomly selected solution must be different from the solution i*/

while(neighbour==i)

neighbour=fix(rand*(FoodNumber))+1;

end;

sol=Foods(i,:);

% /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */

sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;

% /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/

ind=find(sollb);

sol(ind)=lb(ind);

ind=find(solub);

sol(ind)=ub(ind);

%evaluate new solution

ObjValSol=feval(objfun,sol);

FitnessSol=calculateFitness(ObjValSol);

% /*a greedy selection is applied between the current solution i and its mutant*/

if (FitnessSolFitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/

Foods(i,:)=sol;

Fitness(i)=FitnessSol;

ObjVal(i)=ObjValSol;

trial(i)=0;

else

trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/

end;

end;

%%%%%%%%%%%%%%%%%%%%%%%% CalculateProbabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%/* A food source is chosen with the probability which is proportioal to its quality*/

%/*Different schemes can be used to calculate the probability values*/

%/*For example prob(i)=fitness(i)/sum(fitness)*/

%/*or in a way used in the metot below prob(i)=a*fitness(i)/max(fitness)+b*/

%/*probability values are calculated by using fitness values and normalized by dividing maximum fitness value*/

prob=(0.9.*Fitness./max(Fitness))+0.1;

%%%%%%%%%%%%%%%%%%%%%%%% ONLOOKER BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

i=1;

t=0;

while(tFoodNumber)

if(randprob(i))

t=t+1;

%/*The parameter to be changed is determined randomly*/

Param2Change=fix(rand*D)+1;

%/*A randomly chosen solution is used in producing a mutant solution of the solution i*/

neighbour=fix(rand*(FoodNumber))+1;

%/*Randomly selected solution must be different from the solution i*/

while(neighbour==i)

neighbour=fix(rand*(FoodNumber))+1;

end;

sol=Foods(i,:);

% /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */

sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;

% /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/

ind=find(sollb);

sol(ind)=lb(ind);

ind=find(solub);

sol(ind)=ub(ind);

%evaluate new solution

ObjValSol=feval(objfun,sol);

FitnessSol=calculateFitness(ObjValSol);

% /*a greedy selection is applied between the current solution i and its mutant*/

if (FitnessSolFitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/

Foods(i,:)=sol;

Fitness(i)=FitnessSol;

ObjVal(i)=ObjValSol;

trial(i)=0;

else

trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/

end;

end;

i=i+1;

if (i==(FoodNumber)+1)

i=1;

end;

end;

%/*The best food source is memorized*/

ind=find(ObjVal==min(ObjVal));

ind=ind(end);

if (ObjVal(ind)GlobalMin)

GlobalMin=ObjVal(ind);

GlobalParams=Foods(ind,:);

end;

%%%%%%%%%%%% SCOUT BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%/*determine the food sources whose trial counter exceeds the "limit" value.

%In Basic ABC, only one scout is allowed to occur in each cycle*/

ind=find(trial==max(trial));

ind=ind(end);

if (trial(ind)limit)

Bas(ind)=0;

sol=(ub-lb).*rand(1,D)+lb;

ObjValSol=feval(objfun,sol);

FitnessSol=calculateFitness(ObjValSol);

Foods(ind,:)=sol;

Fitness(ind)=FitnessSol;

ObjVal(ind)=ObjValSol;

end;

fprintf('Ýter=%d ObjVal=%g\n',iter,GlobalMin);

iter=iter+1;

end % End of ABC

GlobalMins(r)=GlobalMin;

end; %end of runs

save all

人工蜂群算法的matlab的编程详细代码，最好有基于人工蜂群算法的人工神经网络的编程代码？

蚁群算法(ant colony optimization, ACO)，又称蚂蚁算法，是一种用来在图中寻找优化路径的机率型算法。它由Marco Dorigo于1992年在他的博士论文中提出，其灵感来源于蚂蚁在寻找食物过程中发现路径的行为。蚁群算法是一种模拟进化算法，初步的研究表明该算法具有许多优良的性质。针对PID控制器参数优化设计问题，将蚁群算法设计的结果与遗传算法设计的结果进行了比较，数值仿真结果表明，蚁群算法具有一种新的模拟进化优化方法的有效性和应用价值。

参考下蚁群训练BP网络的代码。