Artificial bee colony algorithm with global and local neighborhoods

Artificial Bee Colony (ABC) is a well known population based efficient algorithm for global optimization. Though, ABC is a competitive algorithm as compared to many other optimization techniques, the drawbacks like preference on exploration at the cost of exploitation and slow convergence are also associated with it. In this article, basic ABC algorithm is studied by modifying its position update equation using the differential evolution with global and local neighborhoods like concept of food sources’ neighborhoods. Neighborhood of each colony member includes $$10\,\%$$ 10 % members from the whole colony based on the index-graph of solution vectors. The proposed ABC is named as ABC with Global and Local Neighborhoods (ABCGLN) which concentrates to set a trade off between the exploration and exploitation and therefore increases the convergence rate of ABC. To validate the performance of proposed algorithm, ABCGLN is tested over $$24$$ 24 benchmark optimization functions and compared with standard ABC as well as its recent popular variants namely, Gbest guided ABC, Best-So-Far ABC and Modified ABC. Intensive statistical analyses of the results shows that ABCGLN is significantly better and takes on an average half number of function evaluations as compared to other considered algorithms.