An effective refined artificial bee colony algorithm for numerical optimisation

Bajer, Dražen; Zorić, Bruno

doi:10.1016/j.ins.2019.07.022

Cited by 44 publications

(11 citation statements)

References 32 publications

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“…The approach is tested on three types of problems including image clustering, Knapsack problem, and numerical optimization. Using some top best solutions in the population, Bajer and Zorić [34] proposed a modified search strategy. By combining X best and two randomly chosen solutions, new solutions are produced in the search phase of scout bees.…”

Section: Brief Review Of Recent Work On Abcmentioning

confidence: 99%

“…The best results are given in bold In the second experiment, KFABC is compared with dABC [50], IABC [51], OABC [52], and DRABC [34]. Table 9 gives the mean error values obtained by KFABC, dABC, IABC, OABC, and DRABC on ten CEC 2013 benchmark problems with D = 10.…”

Section: Comparison Of Kfabc With Other Algorithmsmentioning

confidence: 99%

“…The problems names are listed in the first column of Table 9, and the specific mathematical definitions of these problems can be found in [42]. Results of dABC, IABC, OABC, and DRABC are taken from Table 13 in the literature [34]. From the results, all five ABC algorithms obtain the same solutions on three problems.…”

Section: Comparison Of Kfabc With Other Algorithmsmentioning

confidence: 99%

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Artificial bee colony algorithm based on knowledge fusion

Wang

Zhou

et al. 2020

Complex Intell. Syst.

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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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Section: Brief Review Of Recent Work On Abcmentioning

confidence: 99%

Section: Comparison Of Kfabc With Other Algorithmsmentioning

confidence: 99%

Section: Comparison Of Kfabc With Other Algorithmsmentioning

confidence: 99%

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Artificial bee colony algorithm based on knowledge fusion

Wang

Zhou

et al. 2020

Complex Intell. Syst.

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“…The work of guiding the bee is to transfer and share the information of the discovered food source to other partners waiting in the bee. The information transmitted includes the direction, distance and probability of existence of the honey source (Bajer, D.,2019;Wang, S. W.,2019). After receiving the message leading the bee, the accompanying bee tries to find the specific location of the honey source, and the detection bee is looking for a new source of honey.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Research on GPS Height Fitting Based on Abc Optimized LSSVM

Zhang¹,

Huang²,

Tang³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Aiming at the problem that the fitting parameters of the least squares support vector machine fitting method are difficult to select, a method of introducing the artificial bee colony algorithm into the least squares support vector machine to establish a high-precision region fitting model is proposed. The artificial bee colony algorithm can perform global tracking search on the parameters in the least squares support vector machine, imitate the honey collecting process of the bees, and use the primary value of the parameters as the honey source, and the average square error predicted by the least squares support vector machine as the target. The function value is determined by iterative update within a certain range to determine the optimal parameters, and finally a GPS height fitting model with higher precision is established. Experimental analysis, compared with the conventional least squares support vector machine fitting method, the accuracy of the fitting model constructed by the ABC-LSSVM combination method is improved by 45.4%. At the same time, the combined method is better than the particle swarm optimization fitting method and BP neural network. The legal convergence effect is higher and the stability is better. The effective feasibility of the ABC-LSSVM combination method in the construction of GPS height fitting model is proved, which provides a certain reference value for the establishment of GPS height fitting model. This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W10-1307-2020 | © Authors 2020. CC BY 4.0 License.

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“…Bajer and Zoric proposed an improved ABC based on diversity refining (DRABC) [24]. In DRABC, the solution search equation was modified by introducing the information of the top population members to increase the exploitation ability.…”

Section: Introductionmentioning

confidence: 99%

A Tristage Adaptive Biased Learning for Artificial Bee Colony

Jiang

Lin

et al. 2021

Discrete Dynamics in Nature and Society

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In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.

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An effective refined artificial bee colony algorithm for numerical optimisation

Cited by 44 publications

References 32 publications

Artificial bee colony algorithm based on knowledge fusion

Artificial bee colony algorithm based on knowledge fusion

Research on GPS Height Fitting Based on Abc Optimized LSSVM

A Tristage Adaptive Biased Learning for Artificial Bee Colony

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