Artificial Bee Colony Algorithm with Distant Savants for constrained optimization

Most of the scientific and engineering problems are defined as constrained optimization functions. It can be very difficult due to their complex structures. Artificial Bee Colony Algorithm (ABC) is a remarkable metaheuristic developed for global optimization problems. However, due to the inadequacy of ABC's search capability, it cannot handle constraint optimization problems very well. In this study, an ABC variant adapted for solving constrained optimization problems called Artificial Bee Colony Algorithm with Distant Savants (ABCDS) is proposed to overcome this deficiency. ABCDS is based on a new and adaptable search equation that enables learning with savants that are at a certain distance from each other. Also, the algorithm is hybridized with competitive local search mechanism. To test the performance of ABCDS, benchmark set for Constrained Real-Parameter Optimization defined in CEC 2017 conference (CEC2017COP) and some of the problems in the benchmark set on real-world problems defined in CEC 2020 conference (CEC2020) are used. The results obtained by the algorithm are compared with recent ABC algorithms and some state-of-the-art algorithms. According to the experimental results, ABCDS is better and competitive than the compared algorithms. (C) 2021 Elsevier B.V. All rights reserved.