An artificial bee colony algorithm search guided by scale-free networks

Many optimization algorithms have adopted scale-free networks to improve the search ability. However, most methods have merely changed their population topologies into those of scale-free networks; their experimental results cannot verify that these algorithms have superior performance. In this paper, we propose a scale-free artificial bee colony algorithm (SFABC) in which the search is guided by a scale-free network. The mechanism enables the SFABC search to follow two rules. First, the bad food sources can learn more information from the good sources of their neighbors. Second, the information exchange among good food sources is relatively rare. To verify the effectiveness of SFABC, the algorithm is compared with the original artificial bee colony algorithm (ABC), several advanced ABC variants, and other metaheuristic algorithms on a wide range of benchmark functions. Experimental results and statistical analyses indicate that SFABC obtains a better balance between exploration and exploitation during the optimization process and that, in most cases, it can provide a competitive performance of the benchmark functions. We also verify that scale-free networks can not only improve the optimization performance of ABC but also enhance the search ability of other metaheuristic algorithms, such as differential evolution (DE) and the flower pollination algorithm (FPA).