// //Artificial Bee Colony Algorithm //Main Object: //Minimize the Objective Function //

// EMPLOYED BEE PHASE // FIRST: //A randomly chosen solution is used in producing a mutant solution of the solution i //and randomly selected solution must be different from the solution i -->sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2; //shifted onto the boundaries if generated parameter value is out of boundaries //evaluate new solution THEN: //a greedy selection is applied between the current solution i and its mutant //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 //if the solution i can not be improved, increase its trial counter

// //Calculate Probabilities //A food source is chosen with the probability which is proportioal to its quality // for (i = 0;i<FoodNumber;i++) { NectarSource[i].rfitness = (0.9*(NectarSource[i].fitness / maxfit)) + 0.1; }

// //ONLOOKER BEE PHASE // //A food source is chosen with the probability which is proportioal to its quality NEXT SAME AS EMPLOYED

//--------------->MemorizeBestSource(); // //SCOUT BEE PHASE //determine the food sources whose trial counter exceeds the "limit" value //only one scout is allowed to occur in each cycle //

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

//--------------->MemorizeBestSource();

// COUT<<<<<<<<<<<<<<<<<<<<<<,OVER //