artificial-bee-colony.rar_bee_蜂群_蜂群 改进_蜂群优化_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 limit 100 /*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/ #define maxCycle 3000 /*The number of cycles for foraging {a stopping criteria}*/ /* Problem specific variables*/ #define D 50 /*The number of parameters of the problem to be optimized*/ #define lb -5.12 /*lower bound of the parameters. */ #define ub 5.12 /*upper bound of the parameters. lb and ub can be defined as arrays for the problems of which parameters have different bounds*/ #define M_PI 10 #define runtime 30 /*Algorithm can be run many times in order to see its robustness*/ 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]); /*benchmark functions */ double sphere(double sol[D]); double Rosenbrock(double sol[D]); double Griewank(double sol[D]); double Rastrigin(double sol[D]); /*Write your own�