2018 Volume 30 Issue 2 Pages 556-563
Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm which was inspired by foraging activity of honey bees and an approximate optimizing technique aiming at the real-valued optimization. ABC algorithm has high search performance to the various types of application, however, it includes some problems. For example, there is a problem that ABC algorithm is slow to converge to good solution because it makes the search process with high regard for the diversity of individuals. In recent years, researches on the advanced method of ABC algorithm have been performed briskly, and many hybrid methods which took in the idea of other evolutionary computing method are proposed. In this research, we propose Arithmetic Crossover based ABC algorithm (AC-ABC) which is the advanced method which raised search speed by including arithmetic crossover which is one of the crossover method used by real-coded GA in search processing of ABC algorithm, and Global Search type ABC algorithm (GS-ABC) which raised search performance by using the stochastic search processing used for crossover and mutation in GA into the part of variable selection process in ABC algorithm. We performed the function optimization simulation to confirm the efficiency of proposed method, and it is proved that GS-ABC shows higher search performance than the conventional method in all of six benchmark functions.
