Balaning Explorations with Exploitations in the Artificial Bee Colony Algorithm for Numerical Function Optimization

This paper introduces a variant of Artificial Bee Colony algorithm and compares its results with a number of swarm intelligence and population based optimization algorithms. The Artificial Bee Colony (ABC) is an optimization algorithm based on the intelligent food foraging behavior of honey bees. The proposed variant, Artificial Bee Colony Algorithm with Balanced Explorations and Exploitations (ABC-BEE) makes attempts to dynamically balance the mutation step size with which the artificial bees explore the search space. Mutation with small step size produces small variations of existing solutions which is better for exploitations, while large mutation steps are likely to produce large variations that facilitate better explorations of the search space. ABC-BEE fosters both large and small mutation steps as well as adaptively controls the step lengths based on their effectiveness to produce better solutions. ABC-BEE has been evaluated and compared on a number of benchmark functions with the original ABC algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA). Results indicate that the proposed scheme facilitates more effective mutations and performs better optimization outperforming all the other algorithms in comparison.