A survey on applications and variants of the cuckoo search algorithm

Shehab, Mohammad; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi

doi:10.1016/j.asoc.2017.02.034

Cited by 243 publications

(96 citation statements)

References 106 publications

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“…The specific optimization process is as follows (Dong, Zhang, & Hong, ; Shehab, Khader, & Al‐Betar, ): The training sets and prediction sets of cherry tomatoes samples were divided. The training sets were used for learning samples of SVR. Set the parameter ranges of SVR, including the step size control amount α, the population size of the host nest n , the probability of the host bird found eggs P a and the maximum number of iterations N . Each nest location corresponded to a group of C and g. Levy flights mechanism was applied to update the nest location progressively.…”

Section: Methodsmentioning

confidence: 99%

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SSC prediction of cherry tomatoes based on IRIV‐CS‐SVR model and near infrared reflectance spectroscopy

Sun

et al. 2018

J Food Process Engineering

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As one of the most important indexes of internal quality testing of fruit, soluble solids content (SSC) is significant for its rapid and efficient nondestructive testing by using near infrared reflectance spectroscopy (NIRS). In this article, 126 cherry tomatoes were selected as the research object. Reflectance spectra data of 228 bands in cherry tomatoes were acquired by the near infrared spectrometer and SSC was measured by the hand‐held refractometer. Savitzky–Golay (SG) combined with multiplicative scatter correction (MSC) was used to preprocess the spectral data to reduce the effects of light scattering and other noise. Then, the dimensions of spectral data were reduced by iteratively retaining informative variables (IRIV) algorithm and 10 characteristic wavelengths were obtained, which were 1,080.37, 1,113.62, 1,117.3, 1,297.57, 1,301.02, 1,538.32, 1,540.40, 1,590.72, 1,615.94, and 1,636.89 nm, respectively. Subsequently, support vector regression (SVR) and its two optimization models, PSO‐SVR and CS‐SVR, were respectively used to establish SSC prediction models based on full spectra and characteristic spectra. The experimental results showed the IRIV‐CS‐SVR model for SSC prediction achieved the accuracy with RP2 of 0.9718 and RC2 of 0.9845. Thus, it is feasible to use NIRS with IRIV‐CS‐SVR to make a rapid and efficient nondestructive SSC prediction of cherry tomatoes. Practical applications As one of the important testing standards of fruit internal quality, SSC is of great significance for the rapid and efficient nondestructive testing. In this article, an iteratively retaining information variables (IRIV) algorithm is proposed to extract the characteristic wavelengths, and a regression model CS‐SVR is established by combining the optimization algorithm cuckoo search (CS). This study shows that the model IRIV‐CS‐SVR has a certain effect on SSC prediction of cherry tomatoes.

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Section: Methodsmentioning

confidence: 99%

“…The specific optimization process is as follows (Dong, Zhang, & Hong, 2018;Shehab, Khader, & Al-Betar, 2017):…”

Section: Cs-svr Algorithmmentioning

confidence: 99%

SSC prediction of cherry tomatoes based on IRIV‐CS‐SVR model and near infrared reflectance spectroscopy

Sun

et al. 2018

J Food Process Engineering

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“…[24]. The most recent literature has been carried out by Shehab et al [32], which covers some of the literature up to 2017. These reviews have briefly outlined some of the diverse applications using cuckoo search and its variants.…”

Section: Cuckoo Search In Applicationsmentioning

confidence: 99%

Multi-species Cuckoo Search Algorithm for Global Optimization

Yang¹,

Deb

Mishra

2018

Cogn Comput

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Background: Many optimization problems in science and engineering are highly nonlinear, and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms.Methods: This work extends the standard cuckoo search (CS) by using the successful features of the cuckoo-host co-evolution with multiple interacting species, and the proposed multi-species cuckoo search (MSCS) intends to mimic the multiple species of cuckoos that compete for the survival of the fittest, and they co-evolve with host species with solution vectors being encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal design case studies in practical applications.Results: Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and in this case all optimal solutions are achievable. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm, which demonstrated the efficiency of the present algorithm.Conclusions: Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases, leading a potentially very effective tool for solving nonlinear optimization problems.

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“…There are other variants, and readers can refer to [61]. Here, the rest of this paper will focus on the recent applications.…”

Section: Variantsmentioning

confidence: 99%