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Artificial bee colony (ABC) algorithm is one of the branches of swarm intelligence. Several studies proved that the original ABC has powerful exploration and weak exploitation capabilities. Therefore, balancing exploration and exploitation is critical for ABC. Incorporating knowledge in intelligent optimization algorithms is important to enhance the optimization capability. In view of this, a novel ABC based on knowledge fusion (KFABC) is proposed. In KFABC, three kinds of knowledge are chosen. For each kind of knowledge, the corresponding utilization method is designed. By sensing the search status, a learning mechanism is proposed to adaptively select appropriate knowledge. Thirty-two benchmark problems are used to validate the optimization capability of KFABC. Results show that KFABC outperforms nine ABC and three differential evolution algorithms.

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Artificial bee colony (ABC) algorithm is a popular optimization technique with strong search ability. Although ABC has the ability to handle complex optimization problems, it suffers from weak exploitation and slow convergence. In order to tackle this issue, a new ABC variant based on multiple search strategies and dimension selection (ABC-MSDS) is proposed in this paper. Firstly, multiple search strategies based on dual strategy pool are designed. Compared to other existing ABC with multiple search strategies, our approach constructs two strategy pools for employed and onlooker bees, respectively. Secondly, a new dimension selection method is used to replace the random dimension selection in the standard ABC. In the search process, each dimension is chosen one by one in terms of the quality of offspring. Finally, a modified scout bee phase is employed to accelerate the search. Experimental study is conducted on classical benchmark problems and CEC 2013 shifted and rotated problems. The performance of ABC-MSDS is compared with several recently published ABC variants. Computational results demonstrate the effectiveness of our approach. INDEX TERMS Artificial bee colony (ABC), multiple search strategies, strategy pool, dimensional selection, oppositionbased learning.

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

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Artificial bee colony (ABC) performs excellently over many problems, but it has some shortcomings, such as weak exploitation as well as slow convergence. For the sake of dealing with these issues, a modified ABC known as MPABC is presented. Firstly, the entire population is partitioned into two different subpopulations at the stage of employed bees, and they use different search strategies. Then, a new probability selection strategy is designed on the basis of the principle of Soft Maximum function. Finally, a novel search method is constructed for improving the intensity of exploitation by gradually increasing the ratio of the current optimal solutions. In order to comprehensively validate the capability of MPABC, 12 benchmark problems are employed. Computational results clearly demonstrate MPABC surpasses the basic ABC and some other famous ABCs.