Enhancing artificial bee colony algorithm using refraction principle

Shao, Peng; Yang, Lu; Tan, Liang; Li, Guangquan; Peng, Hu

doi:10.1007/s00500-020-04863-2

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…Artificial Bee Colony (ABC) divides bee colony into three categories by simulating the honeybee's honey collecting mechanism: honeybee, observation bee and reconnaissance bee. Assuming that the solution space of the problem is D-dimensional, the numbers of collecting bees and observing bees are both SN and are equal to the number of nectar sources [37][38][39][40][41][42][43]. The location of each nectar source represents a possible solution, and the amount collected from the source corresponds to the fitness of the corresponding solution, and there is a one-to-one correspondence between the honeybee and the nectar source.…”

Section: Intelligent Optimization Methods 231 Artificial Bee Colonymentioning

confidence: 99%

Improved Hargreaves Model Based on Multiple Intelligent Optimization Algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

Cui

Zhu

et al. 2020

Atmosphere

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Reference crop evapotranspiration (ET0) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET0 estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961–2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET0 value was calculated by FAO-56 Penman–Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley–Taylor, Imark–Allen and Jensen–Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R2 range 0.74–0.98) are more accurate than 4 empirical models (R2 range 0.55–0.84), and ET0-PSO-HG has the best accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with average R2 of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively; on a monthly scale, ET0-PSO-HG also has the highest accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with median R2 of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET0 estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET0 in humid regions in southwest China regions.

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Section: Intelligent Optimization Methods 231 Artificial Bee Colonymentioning

confidence: 99%

Improved Hargreaves Model Based on Multiple Intelligent Optimization Algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

Cui

Zhu

et al. 2020

Atmosphere

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“…• limit = 50. 50,51 • limit = 100 39,52 • limit = 200 18,53 •  limit SN D = . 54,55 So, all the above four values are test for the BSABC.…”

Section: Sensitivity Test For the Parameter Limitmentioning

confidence: 99%

Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

Zhou

Huang

Tang

et al. 2022

Int J of Intelligent Sys

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As an effective global optimization technique, artificial bee colony (ABC) algorithm has become one of the hottest research topics in the fields of evolutionary algorithms. However, the solution search equation is not rotationally invariant, which causes the problem that the performance of ABC is sensitive to the coordinate system. Although many improved ABC variants have been developed, they rarely considered the problem. Hence, to solve the problem, we propose a new ABC variant with bi‐coordinate systems (BSABC), including the original coordinate system and the eigen coordinate system. The two coordinate systems own different characteristics: (1) the former one aims to maintain the population diversity, and (2) the latter one is to adapt the search to the fitness landscape of the problems. Based on the characteristics, in the BSABC, the two coordinate systems are used in the employed bee phase and onlooker bee phase, respectively. Meanwhile, two new solution search equations are designed by utilizing the elite information, and they are respectively performed in the two coordinate systems to further improve the algorithm performance. As another contribution of this study, in the scout bee phase, the multivariate Gaussian distribution is constructed to replace the original method to generate offspring, which is helpful to save the search experience. The performance of the BSABC is verified by extensive experiments on the CEC2013 test suite and one real‐world optimization problem, and four well‐established ABC variants and three other evolutionary algorithms are included in the performance comparison. The comparison results confirm that the BSABC shows competitive performance by achieving better results on the majority of test functions.

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“…The experimental results show that OAQABC has better performance than basic ABC, quick ABC, CS, and PSO. Shao et al (2020) enhanced the ABC algorithm using refraction principle (EABC-RP). In order to intensify its exploration, the unified OBL (UOBL) based on refraction principle is adopted to produce refraction solutions for employed bees, which provides diversity in the population and guides the search towards the global optimal solution.…”

Section: Using Opposition-based Learning In Abcmentioning

confidence: 99%

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

2020

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Enhancing artificial bee colony algorithm using refraction principle

Cited by 16 publications

References 31 publications

Improved Hargreaves Model Based on Multiple Intelligent Optimization Algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

Improved Hargreaves Model Based on Multiple Intelligent Optimization Algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

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