ABC.zip_ABC_人工蜂群算法_新型算法_蜂群算法

/* ABC algorithm coded using C programming 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) */ #include <stdio.h> #include <stdlib.h> #include <math.h> #include <conio.h> #include <time.h> /* Control Parameters of ABC algorithm*/ #define NP 40 /* The number of colony size (employed bees+onlooker bees)*/ #define FoodNumber NP/2 /*The number of food sources equals the half of the colony size*/ #define maxCycle 500 /*The number of cycles for foraging {a stopping criteria}*/ /* Problem specific variables*/ #define D 10 /*The number of parameters of the problem to be optimized*/ #define lb -100 /*lower bound of the parameters. */ #define ub 100 /*upper bound of the parameters. lb and ub can be defined /*as arrays for the problems of which parameters have different bounds*/ #define limit 110 /*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/ #define M_PI 3.1415926 #define OPT 0.0 #define ESP 1.0E-6 #define runtime 30 /*Algorithm can be run many times in order to see its robustness*/ #define true 1 #define false 0 typedef int bool; double 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*/ double f[FoodNumber]; /*f is a vector holding objective function values associated with food sources */ double fitness[FoodNumber]; /*fitness is a vector holding fitness (quality) values associated with food sources*/ double trial[FoodNumber]; /*trial is a vector holding trial numbers through which solutions can not be improved*/ double prob[FoodNumber]; /*prob is a vector holding probabilities of food sources (solutions) to be chosen*/ double 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*/ double ObjValSol; /*Objective function value of new solution*/ double FitnessSol; /*Fitness value of new solution*/ int neighbour, param2change; /*param2change corrresponds to j, neighbour corresponds to k in equation v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij})*/ double GlobalMin; /*Optimum solution obtained by ABC algorithm*/ double GlobalParams[D]; /*Parameters of the optimum solution*/ double GlobalMins[runtime]; /*GlobalMins holds the GlobalMin of each run in multiple runs*/ double r; /*a random number in the range [0,1)*/ /*a function pointer returning double and taking a D-dimensional array as argument */ /*If your function takes additional arguments then change function pointer definition and lines calling "...=function(solution);" in the code*/ typedef double (*FunctionCallback)(double sol[D]); int evalueTimes = 0; /*benchmark functions */ double sphere(double sol[D]); double Rosenbrock(double sol[D]); double Griewank(double sol[D]); double Rastrigin(double sol[D]); int stopgener[runtime]; int evalues[runtime]; /*Write your own objective function name instead of sphere*/ //FunctionCallback function = &Rastrigin; FunctionCallback function = &sphere; bool judgeStop(int *iter); void findbestworst(double sol[runtime],double *best,double *worst) { int i; *best = sol[0]; *worst = sol[0]; for ( i = 1 ; i < runtime; i++) { if(sol[i] < *best) *best = sol[i]; if ( sol[i] > *worst) { *worst = sol[i]; } } } double avg(int src[runtime]) { double ret = 0; int i = 0; for ( i = 0; i < runtime; i++) { ret += src[i]; } return ret/runtime; } /*Fitness function*/ double CalculateFitness(double fun) { double result=0; evalueTimes++; if(fun>=0) { result=1/(fun+1); } else { result=1+fabs(fun); } return result; } /*The best food source is memorized*/ void MemorizeBestSource() { int i,j; for(i=0;i<FoodNumber;i++) { if (f[i]<GlobalMin) { GlobalMin=f[i]; for(j=0;j<D;j++) GlobalParams[j]=Foods[i][j]; } } } /*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 */ /* Counters of food sources are also initialized in this function*/ void init(int index) { int j; for (j=0;j<D;j++) { r = ( (double)rand() / ((double)(RAND_MAX)+(double)(1)) ); Foods[index][j]=r*(ub-lb)+lb; solution[j]=Foods[index][j]; } f[index]=function(solution); fitness[index]=CalculateFitness(f[index]); trial[index]=0; } /*All food sources are initialized */ void initial() { int i; for(i=0;i<FoodNumber;i++) { init(i); } GlobalMin=f[0]; for(i=0;i<D;i++) GlobalParams[i]=Foods[0][i]; } void SendEmployedBees() { int i,j; /*Employed Bee Phase*/ for (i=0;i<FoodNumber;i++) { /*The parameter to be changed is determined randomly*/ r = ((double)rand() / ((double)(RAND_MAX)+(double)(1)) ); param2change=(int)(r*D); /*A randomly chosen solution is used in producing a mutant solution of the solution i*/ r = ( (double)rand() / ((double)(RAND_MAX)+(double)(1)) ); neighbour=(int)(r*FoodNumber); /*Randomly selected solution must be different from the solution i*/ while(neighbour==i) { r = ( (double)rand() / ((double)(RAND_MAX)+(double)(1)) ); neighbour=(int)(r*FoodNumber); } for(j=0;j<D;j++) solution[j]=Foods[i][j]; /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */ r = ( (double)rand() / ((double)(RAND_MAX)+(double)(1)) ); solution[param2change]=Foods[i][param2change]+(Foods[i][param2change]-Foods[neighbour][param2change])*(r-0.5)*2; /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/ if (solution[param2change]<lb) solution[param2change]=lb; if (solution[param2change]>ub) solution[param2change]=ub; ObjValSol=function(solution); FitnessSol=CalculateFitness(ObjValSol); /*a greedy selection is applied between the current solution i and its mutant*/ if (FitnessSol>fitness[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*/ trial[i]=0; for(j=0;j<D;j++) Foods[i][j]=solution[j]; f[i]=ObjValSol; fitness[i]=FitnessSol; } else { /*if the solution i can not be improved, increase its trial counter*/ trial[i]=trial[i]+1; } } /*end of employed bee phase*