Artificial Bee Colony Optimization Algorithm with New Full Dimension Updating Strategy and Its Application

Abstract

Abstract For the low accuracy and slow convergence of artificial bee colony (ABC) algorithm in solving complex optimization problems, a new full dimensional updating ABC/best/1 evolutionary strategy is designed to propose an improved ABC based on the new full dimensional updating strategy(FNABC) in this paper. Because of the low efficiency of one-dimensional search, the full dimensional update search strategy and ABC/best /1 evolutionary strategy are used to design a new full dimensional update ABC/best/1 evolutionary strategy, which expands the search space, improves the mining ability and search efficiency. And a new evolutionary phase of full dimensional update strategy is designed to balance the global search ability and mining ability. Finally, the FNABC is compared with eight state-of-the-art ABC variants in solving 12 functions. The experiment results indicate that the FNABC has better search ability. Additionally, the FNABC is applied to solve a real-world train operation adjustment problem. The results show that it can obtain the ideal results of the train operation adjustment problem.