Artificial bee colony (ABC) algorithm is one representative of many wellknown swarm intelligence methods for continuous optimization problems. However, it cannot directly solve discrete optimization problems without using complex transfer functions. Furthermore, the solutions quality and deviations obtained by many famous intelligent algorithms are still to be enhanced for solving uncapacitated facility location problems (UFLP). To this end, a continuous ABC called cABC is proposed for UFLP. In cABC, a chaotic initialization technique is employed to produce a good initial population in the range of [0,1), which enables cABC to evolve in continuous space. Then, a common probability discretizing mechanism is used to convert a continuous individual to a 0-1 vector, which enables cABC to solve UFLP. In addition, for infeasible solutions, a dynamic repair strategy is presented. Next, to enhance search performance of ABC, a random guiding mechanism is proposed. Subsequently, a time varying perturbation scheme is presented to share much more information between current individual and guiding individual. Next, a modified probability choice mechanism with random character is employed before entering onlooker bees phase. Last, an opposition based learning technique is employed to improve continuous nonupdating individual at the scout bees phase. To test effectiveness of cABC, it is first compared with traditional ABC on famous CAP dataset consisting of fifteen instances. To further validate superiority of cABC, it is compared with other eleven famous approaches on CAP dataset and M* dataset with twenty instances. Experimental results show that cABC surpasses other state-of-the-art methods in terms of solution accuracy and robustness.
An et al. (Fri,) studied this question.