Abstract
While the outcomes of artificial bee colony (ABC) have been encouraging enough, ABC algorithm lacks good compromise between exploration and exploitation. The main aim of this paper is to propose ABC-based algorithm; namely knowledge-based artificial bee colony (K-ABC), that is able to converge quickly and explore the most promising area of the intended search space. To be sure that the K-ABC algorithm is trustworthy and reliable, it is tested against the most recent well-known benchmarks CEC’2017. On top of that, the K-ABC wins against their counterpart on the inverse kinematic problem for a 5 DOF robot arm and some other complex constrained engineering problems. The effect of the limit parameter is investigated as well. Experimental results show that K-ABC has a mean squared error 60% less than standard ABC for the inverse kinematic solution.
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