function?gy?=ABC_Algorithm（Food_Numlimitmax_iterDobjfunLowerUpperr）
%%?變量解釋
%?-----------------------------------------------------------------------------------------
%?NP?蜂群大小?（雇傭峰+觀察峰）*/
%?FoodNumber為食物源數量?=?蜂群/2
%?limit為食物源遺棄條件
%?max_iter???%?搜索次數
%Foods?[FoodNumber][D];?/*Foods[食物源數量][D維待優化參數].
%ObjVal[FoodNumber];????/*每個食物源的目標函數值
%Fitness[FoodNumber];???/*每個食物源的適應度值（質量）
%trial[FoodNumber];??????/*每個解沒有被提高的次數*/
%prob[FoodNumber];???????/*食物源被選擇的概率
%New_solution?[D];???????????/*新解有公式產生?v{ij}=x{ij}+a{ij}*（x{kj}-x{ij}）?j是一個隨機參數且k與i不同*/
%ObjValSol;??????????????/*新解的目標函數值
%FitnessSol;??????????????/*新解的適應度值
%neighbour?param2change;?/*param2change與j對應?neighbour與k對應在公式v_{ij}=x_{ij}+\phi_{ij}*（x_{kj}-x_{ij}）中*/
%GlobalMin;?/*ABC算法的最優解*/
%GlobalParams[D];?/*最優解的參數*/
%GlobalMins[runtime];?/*在多次運行后得每次最優解
%---------------------------------------------------------------------------

U_bound?=ones（1D）*Upper;%32.768;??????%參數的下界
L_bound?=ones（1D）*Lower;%32.768???%參數的上界

Foods_min=ones（max_iterD）;
Select_P=zeros（1Food_Num）;

Range?=?repmat（（U_bound-L_bound）[Food_Num?1]）;%?變量范圍
Lower?=?repmat（L_bound?[Food_Num?1]）;
Foods?=?rand（Food_NumD）.*?Range?+?Lower;??????????????%?食物源初始化;

Obj_Val=feval（objfunFoods）;??????????????????????????????%目標函數值
Fitness=Cal_Fitness（Obj_Val）;????????????????????????%適應度值
Global_Min=ones（1max_iter）;???%每次迭代最優解
trial=zeros（1Food_Num）;????%重置計數器

%------------記錄最好的食物源---------
Best_Ind=find（Obj_Val==min（Obj_Val））;
Best_Ind=Best_Ind（end）;
GlobalMin_iter=Obj_Val（Best_Ind）;
GlobalParams=Foods（Best_Ind:）;

%%?迭代開始
iter=1;
while?iter?<=?max_iter
????%%??%%%%%%%%%%%%%雇傭峰（偵查蜂）階段?%%%%%%%%%%%%%%
????for?i=1:（Food_Num）
????????Param2Change=fix（rand*D）+1;?????????%?參數是隨機改變
????????neighbour=fix（rand*（Food_Num））+1;???%?隨機選擇的解用于產生解i的變異解
????????
????????while（neighbour==i）?%隨機選擇一個與解i不同的解
????????????neighbour=fix（rand*（Food_Num））+1;
????????end
????????New_sol=Foods（i:）;
????????%%??/*產生新解*/
????????
????????New_sol（Param2Change）=Foods（iParam2Change）+（Foods（iParam2Change）-Foods（neighbourParam2Change））*（rand-0.5）*2;
????????New_sol=bound_limit（New_solL_boundU_bound）;%?邊界值處理
????????
????????%%?/*計算新解*/
????????
????????ObjVal_Sol=feval（objfunNew_sol）;
????????Fitness_Sol=Cal_Fitness（ObjVal_Sol）;
????????
????????%%?/*貪婪算法*/
????????
????????if?（Fitness_Sol?>?Fitness（i））???%if新解優于解i，則替代i解
????????????Foods（i:）=New_sol;
????????????Fitness（i）=Fitness_Sol;
????????????Obj_Val（i）=ObjVal_Sol;
????????????trial（i）=0;
????????else
????????????trial（i）=trial（i）+1;?%如果解i沒有提高trial+1
????????end
????????%%?%%%%%%%%%%%%%%%%%?計算概率?%%%%%%%%%%%%%
????????
????????%食物源是根據概率（質量）被選擇的
????????Pro=2;
????????switch?Pro
????????????case?1
????????????????Select_P（i）=Fitness（i）/sum（Fitness）;
????????????case?2
????????????????Select_P（i）=（0.9.*Fitness（i）./max（Fitness））+0.1;
????????end
????end
????%%?%%%%%%%%%%%%%%?觀察蜂階段?%%%%%%%%%%
????
????i=1;
????

?屬性????????????大小?????日期????時間???名稱
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?????文件???????5961??2016-06-22?20:54??人工蜂群算法求解無約束優化問題\ABC_Algorithm.m

?????文件????????214??2016-06-22?18:42??人工蜂群算法求解無約束優化問題\bound_limit.m

?????文件????????201??2016-06-22?18:42??人工蜂群算法求解無約束優化問題\Cal_Fitness.m

?????文件????????273??2016-06-11?19:18??人工蜂群算法求解無約束優化問題\Problem1.m

?????文件????????100??2016-06-11?18:51??人工蜂群算法求解無約束優化問題\Problem2.m

?????文件????????326??2016-06-22?09:39??人工蜂群算法求解無約束優化問題\Problem3.m

?????文件???????1074??2017-12-27?22:53??人工蜂群算法求解無約束優化問題\run_ABC.m

?????文件?????164352??2017-12-27?22:54??人工蜂群算法求解無約束優化問題\遺傳算法求解結果.doc

?????目錄??????????0??2017-12-27?22:55??人工蜂群算法求解無約束優化問題

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???????????????172501????????????????????9