A hybrid artificial bee colony algorithm for numerical function optimization

Alqattan, Zakaria N. M.; Abdullah, Rosni

doi:10.1142/s0129183115501090

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…Over twenty test problems and four actual optimization issues were used to evaluate the performance of HABCDE. Alqattan et al [25] presented a hybrid particle movement ABC algorithm (HPABC). This algorithm adapted the particle moving process to improve the exploitation of the original ABC variant.…”

Section: Abc Variantsmentioning

confidence: 99%

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

Huang,

Yu,

Guo

et al. 2023

Applied Sciences

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As a newly developed metaheuristic algorithm, the artificial bee colony (ABC) has garnered a lot of interest because of its strong exploration ability and easy implementation. However, its exploitation ability is poor and dramatically deteriorates for high-dimension and/or non-separable functions. To fix this defect, a self-adaptive ABC with a candidate strategy pool (SAABC-CS) is proposed. First, several search strategies with different features are assembled in the strategy pool. The top 10% of the bees make up the elite bee group. Then, we choose an appropriate strategy and implement this strategy for the present population according to the success rate learning information. Finally, we simultaneously implement some improved neighborhood search strategies in the scout bee phase. A total of 22 basic benchmark functions and the CEC2013 set of tests were employed to prove the usefulness of SAABC-CS. The impact of combining the five methods and the self-adaptive mechanism inside the SAABC-CS framework was examined in an experiment with 22 fundamental benchmark problems. In the CEC2013 set of tests, the comparison of SAABC-CS with a number of state-of-the-art algorithms showed that SAABC-CS outperformed these widely-used algorithms. Moreover, despite the increasing dimensions of CEC2013, SAABC-CS was robust and offered a higher solution quality.

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Section: Abc Variantsmentioning

confidence: 99%

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

Huang,

Yu,

Guo

et al. 2023

Applied Sciences

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“…Hybrid Particle-move Artificial Bee Colony (HPABC) Algorithm was initially proposed as an optimization algorithm for numerical functions (Alqattan & Abdullah, 2015). The algorithm is basically inspired by the unique solution improvement or the particle move operation that the Particle Swarm Optimization (PSO) algorithm performs during its search operation.…”

Section: Hpabc Algorithmmentioning

confidence: 99%

“…In the HPABC algorithm (Alqattan & Abdullah, 2015), the colony of the artificial bee contains only two groups of bees: onlookers and scouts. The hall colony members are onlooker bees.…”

Section: Hpabc Algorithmmentioning

confidence: 99%

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Neural Network Training Using Hybrid Particlemove Artificial Bee Colony Algorithm for Pattern Classification

Nuaimi

Abdullah

2017

JICT

Self Cite

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The Artificial Neural Networks Training (ANNT) process is an optimization problem of the weight set which has inspired researchers for a long time. By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks. Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima. Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues. Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. However, hybrid algorithms are also a fundamental concern in the optimization field, which aim to cumulate the advantages of different algorithms into one algorithm. In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application. The performance of the HPABC algorithm was investigated on four benchmark pattern-classification datasets and the results were compared with other algorithms. The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT. HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.

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“…41 The number of employed bees is the same as the number of food sources, with one employed bee for one food source. 42 The employed bee of a rejected food source becomes a scout. 43 The process of food resources of the employed artificial bees are summarized by four steps, as shown in Table 5.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Multi-objective path finding in stochastic networks using a biogeography-based optimization method

Wang

Yang

et al. 2016

SIMULATION

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Multi-objective path finding (MOPF) problems are widely applied in both academic and industrial areas. In order to deal with the MOPF problem more effectively, we propose a novel model that can cope with both deterministic and random variables. For the experiment, we compared five intelligence-optimization algorithms: the genetic algorithm, artificial bee colony (ABC), ant colony optimization (ACO), biogeography-based optimization (BBO), and particle swarm optimization (PSO). After a 100-run comparison, we found the BBO is superior to the other four algorithms with regard to success rate. Therefore, the BBO is effective in MOPF problems.

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A hybrid artificial bee colony algorithm for numerical function optimization

Cited by 15 publications

References 42 publications

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

Neural Network Training Using Hybrid Particlemove Artificial Bee Colony Algorithm for Pattern Classification

Multi-objective path finding in stochastic networks using a biogeography-based optimization method

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