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The artificial bee colony (ABC) is a population-based metaheuristic that mimics the foraging behaviour of honeybees in order to produce high-quality solutions for optimisation problems. The ABC algorithm combines both exploration and exploitation processes. In the exploration process, the worker bees are responsible for selecting a random solution and applying it to a random neighbourhood structure, while the onlooker bees are responsible for choosing a food source based on a selection strategy. In this paper, a disruptive selection strategy is applied within the ABC algorithm in order to improve the diversity of the population and prevent premature convergence in the evolutionary process. A self-adaptive strategy for selecting neighbourhood structures is added to further enhance the local intensification capability (adaptively choosing the neighbourhood structure helps the algorithm to escape local …