Explorative Artificial Bee Colony Algorithm: A Novel Swarm Intelligence Based Algorithm for Continuous Function Optimization

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence based algorithm that has been successfully employed to numerous scientific and engineering problems. However, ABC sometimes suffers from premature convergence and fitness stagnation, which usually originates from the lack of explorative search capability of its perturbation operator. This paper introduces Explorative ABC (EABC) a novel variant of the basic ABC algorithm that modifies its exploitative perturbation operation in a more explorative way. EABC not only introduces more randomness during the perturbation operation of ABC, but also customizes the degree of exploitations and explorations at the individual solution level, separately for each candidate solution of the bee population. Besides, EABC introduces a crossover operation and a number of additional alternations of the basic ABC algorithm to assist its explorative perturbation operation. EABC is evaluated with several benchmark problems on numerical function optimization and the results are compared with the basic ABC algorithm. The experimental results demonstrate that EABC often performs better optimization than the basic ABC algorithm on most of the benchmark problems, which indicates the effectiveness of its explorative perturbation operation.