A Review of the Development and Applications of the Cuckoo Search Algorithm

Walton, Sean; Hassan, O.; Morgan, K.; Brown, M. Rowan

doi:10.1016/b978-0-12-405163-8.00011-9

Cited by 23 publications

(12 citation statements)

References 26 publications

(45 reference statements)

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“…Unfortunately, there is some discrepancy in the literature on CS. In later publications on CS algorithm, the descriptions of the algorithm are not entirely consistent with the initial as noted by Walton et al, and sometimes discrepancies between the description and implementation exist even within the same reference, as in the case of Yang . The CS algorithm described and used in this work is based on the MATLAB code provided in the appendix of Yang, the same version of the CS can also be found in CS algorithm, version 1.3…”

Section: Approach To the Wind Farm Layout Optimizationmentioning

confidence: 99%

Cuckoo search for wind farm optimization with auxiliary infrastructure

2018

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This paper focuses on the optimization problem of a wind farm layout. This area of research is currently receiving widespread attention, as optimal positioning of the turbines promotes the financial viability of the wind farm and enhances the competitiveness of wind projects in the energy market. In this work, cuckoo search (CS), a modern population‐based metaheuristic optimization algorithm, is used. The objective is to find the turbine layout and types that maximize the net present value of the wind farm, while constraints on the turbine positions have to be met. The following constraints are considered: Firstly, the minimum distance between turbines for safe operation; secondly, a realistic wind farm shape including forbidden zones for installation and the existing infrastructure. Furthermore, the optimization of the wind farm includes an algorithm to find the least expensive layout of the wind farm roads and the electrical collector system. The algorithm is based on Dijkstra's shortest path and Prim's minimum spanning tree algorithms. The test results indicate that the infrastructure cost has a significant effect on the optimum wind farm solution. A genetic algorithm, commonly applied to wind farm micro‐siting problems, is used to benchmark the performance of the CS. The results show that the CS is capable of consistently finding better solutions than the genetic algorithm.

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Section: Approach To the Wind Farm Layout Optimizationmentioning

confidence: 99%

Cuckoo search for wind farm optimization with auxiliary infrastructure

2018

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“…In the above algorithm chaotic sequence within the range [0, 0.5] has been used as step length because it has ergodicity property. Lin and Lee (2012) proposed to use chaotic sequence as the step size of lé vy flight [27].…”

Section: C) Firefly Algorithm With Chaotic Lé Vy Flightmentioning

confidence: 99%

A Chaotic Lévy flight Approach in Bat and Firefly Algorithm for Gray level image Enhancement

Dhal¹,

Quraishi²,

Das³

2015

IJIGSP

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Abstract-Recently nature inspired metaheuristic algorithms have been applied in image enhancement field to enhance the low contrast images in a control manner. Bat algorithm (BA) and Firefly algorithm (FA) is one of the most powerful metaheuristic algorithms. In this paper these two algorithms have been implemented with the help of chaotic sequence and lé vy flight. One of them is FA via lé vy flight where step size of lé vy flight has been taken from chaotic sequence. In the Bat algorithm the local search has been done via lé vy flight with chaotic step size. Chaotic sequence shows ergodicity property which helps in better searching. These two algorithms have been applied to optimize parameters of parameterized high boost filter. Entropy, number of edge pixels of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it is clear that BA with chaotic lé vy outperforms the FA via chaotic lé vy.

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“…Cuckoo Search (CS) es una metaheurística relativamente nueva que ha sido aplicada en diversos ámbitos, tales como medio ambiente (Jiang et al, 2014) y electricidad . CS es un algoritmo de optimización libre del cálculo del gradiente, esta característica ha influenciado para su crecimiento en popularidad (Walton et al, 2013).…”

Section: Introductionunclassified

“…Cuckoo Search (CS) is relatively a new metaheuristic that has been applied in diverse fields such as the environment (Jiang et al, 2014) and electricity . CS is a gradient free method, this feature has influenced its growing popularity (Walton et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Singular Spectrum Analysis and Autoregressive models for Ecuadorian shrimp catch forecasting

Barba¹,

Rodríguez²

2018

Bol. geol. min.

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Shrimp trawl fishing is a relevant activity that contributes significantly to the generation of economic resources for Ecuador through the creation of jobs and the commercialization of the shrimps. Unfortunately the high variability of the marine ecosystem signals are complex making forecasting a difficult task. This paper evaluates the use of singular spectrum analysis (SSA) for improving forecasting by conventional methods. SSA is implemented in four steps, embedding, decomposition, grouping, and diagonal averaging. The effective length of the embedding window is selected by means of the information provided by the differential energy of the eigenvalues. The SSA decomposition is used to extract two types of components, one of low frequency which represents the inter-annual component, and the other of high frequency which represents the annual component. Once the components have been obtained, three models based on SSA are implemented, the first is based on the autoregressive model (SSA-AR), the second is an artificial neural network (ANN) based on the Levenberg-Marquardt algorithm (SSA-ANN-LM), and the third is another ANN based on the Cuckoo Search algorithm (SSA-ANN-CS). The historical data that was used to evaluate the models are the shrimp monthly catches in the Gulf of Guayaquil from 2000 to 2012. The empirical results show the superiority of the proposed models with respect to the conventional models. The best approximation was achieved with the enhanced model SSA-AR, with a MAPE of 1.0%, a R 2 of 99% and a RMSE of 1.5%.El aprendizaje de la red es evaluado por medio de dos algoritmos, uno basado en el descenso del gradiente (Levenberg-Marquardt) y otro basado en una metaheurística (Cuckoo Search). Métricas de RendimientoLa exactitud del pronóstico es evaluada por medio de las métricas: Mean Absolute Percentage Error (MAPE),

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A Review of the Development and Applications of the Cuckoo Search Algorithm

Cited by 23 publications

References 26 publications

Cuckoo search for wind farm optimization with auxiliary infrastructure

Cuckoo search for wind farm optimization with auxiliary infrastructure

A Chaotic Lévy flight Approach in Bat and Firefly Algorithm for Gray level image Enhancement

Singular Spectrum Analysis and Autoregressive models for Ecuadorian shrimp catch forecasting

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