A Novel Artificial Bee Colony Algorithm with Division of Labor for Solving CEC 2019 100-Digit Challenge Benchmark Problems

As a relatively new paradigm of evolutionary algorithms, artificial bee colony (ABC) algorithm has shown attractive performance in solving optimization problems. However, for some complex optimization problems, its performance is still not satisfactory. To address this concerning issue, in this paper, we designed an improved ABC variant, called DLABC, based on the division of labor theory. In the DLABC, both of the employed bee phase and the onlooker bee phase are modified by introducing new solution search equations. For the modified employed bee phase, two new control parameters are introduced in the new solution search equation, which aims to control the frequency of perturbation and the magnitude of perturbation, respectively. For the modified onlooker bee phase, a novel depth-first search framework is used which tends to allocate more computing resources to elite solutions for accelerating convergence rate. By combing these two modified phases, we attempt to balance the exploration and exploitation capabilities of ABC. Numerical experiments are conducted on the CEC 2019 100-digit challenge benchmark suite, and the DLABC is compared with the basic ABC and a recently well-established ABC variant (DFSABC_elite). The total scores of these included algorithms have shown that our proposed DLABC algorithm performed best.