As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of\nlabor and information exchange during the process of honey collection has advantage of simple structure, less control\nparameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so\non. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location\nupdating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as\na search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm\npopulation, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain\nthe population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained\nbenchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better\neffect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase\nof dimension.
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