Artificial bee colony (ABC) algorithm is a widely used swarm intelligence algorithm due to its simple structure, good robustness and strong exploration ability. However, the unbalanced exploration and exploitation capabilities restrict its performance. To tackle this problem, a Neighborhood Evidence-Driven Artificial Bee Colony (NEDABC) algorithm is proposed in the framework of theory of belief functions, integrating information fusion with ABC algorithm. In the onlooker bee phase, instead of being guided by the current optimal solution or specific elite solutions, onlooker bees select their search directions by fusing the comprehensive evidence provided by food sources within their neighborhood. Both short-range solutions and high-quality solutions can have a great impact on the selection of search direction. With this constructive search strategy, onlooker bees will enhance the exploration and exploitation of their nearby potential areas. Under the joint action of the population, the potential optimal region will soon be found and exploited. The proposed algorithm is tested on a set of benchmark functions and compared with several variants of ABC. Experimental results reveal its powerful abilities in achieving higher accuracy and faster convergency. In addition, the NEDABC algorithm is applied to operation optimization of a wet flue gas desulfurization system, demonstrating the practicability and efficiency of the proposed algorithm in engineering applications.
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