A NOVEL ARTIFICIAL BEE COLONY-BASED ALGORITHM FOR SOLVING THE NUMERICAL OPTIMIZATION PROBLEMS

Artificial Bee Colony (ABC) is one of the popular algorithms of swarm intelligence. The ABC algorithm simulates foraging and dance behaviors of real honey bee colonies. It has high performance and success for numerical benchmark optimization problems. Although solution exploration of ABC algorithm is good, exploitation to found food sources is poor. In this study, inspiring Genetic Algorithm (GA), we proposed a crossover operation-based neighbor selection technique for information sharing in the hive. Local search and exploitation abilities of the ABC were herewith improved. The experimental results show that the improved ABC algorithm generates the solutions that are significantly more closed to minimal ones than the basic ABC algorithm on the numerical optimization problems and estimation of energy demand problem.