Performance Comparison of Cuckoo Search and Differential Evolution Algorithm for Constrained Optimization

Solihin, Mahmud Iwan; Zanil, Mohd Fauzi

doi:10.1088/1757-899x/160/1/012108

Cited by 24 publications

(15 citation statements)

References 11 publications

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“…In fact, differential evolution, simulated annealing and PSO are special cases of CS algorithm, hence it is not surprising why CS algorithm outperforms them (Yang, 2014). In Solihin and Zanil (2016), CS algorithm outperformed Differential evolution algorithm in terms of convergence speed to reach optimum solution. In addition, CS algorithm was reported as being more computationally efficient than the PSO (Adnan and Razzaque, 2013).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Constrained cuckoo search algorithm and Takagi-Sugeno fuzzy models for predictive control

Taeib

Salhi

Châari

2020

Transactions of the Institute of Measurement and Control

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In this paper, a new predictive control scheme formulated by using the Takagi-Sugeno fuzzy modeling method and a new constrained cuckoo search algorithm. The cuckoo search algorithm is used to determine the predictive controls by minimizing a constrained criterion. The Takagi-Sugeno fuzzy modelling approach is applied to forecast the states of the process. At the optimization stage, the proposed cuckoo search provides the control action taking into account constraints. The performances of the developed method are tested during its application in the three-tank process. Therefore, the experimental results demonstrate that the combination of the philosophy of the fuzzy model and cuckoo search is very good in the controlling of nonlinear processes. In addition, the closed-loop performance of the developed method is compared to approach based with the particle swarm optimisation algorithm and those obtained with fuzzy model predictive controller.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Constrained cuckoo search algorithm and Takagi-Sugeno fuzzy models for predictive control

Taeib

Salhi

Châari

2020

Transactions of the Institute of Measurement and Control

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“…In this work, a minimum data set generated using CCD was applied. These data were fitted into a model using IT2 fuzzy‐KM and the framework was regressed using a gradient descent technique . This technique was carried out to minimise the integral square of error (ISE).…”

Section: Resultsmentioning

confidence: 99%

Interval type‐2 fuzzy logic with Karmen‐Mendel algorithm for sequential ionic liquid dissolution–solid acid saccharification

Lee

Zanil

2018

J of Chemical Tech & Biotech

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Background Acquiring a good model is important for representing a real system especially in complex chemical processes. In this work, type‐2 fuzzy logic with the Karmen‐Mendel algorithm is proposed to model sequential ionic liquid dissolution–solid acid saccharification. The fuzzy calculation framework was regressed using a gradient descent technique to find the best fit model for a given data set with the target to achieve minimum integral square error (ISE) between predicted and experiment data. Results The regression results for both ionic liquid dissolution and solid acid saccharification were ISE 107.62 at a computational time of 907.35 s, and ISE 69.65 at 672.90 s, respectively. The high R‐squared value of 0.98829 (ionic liquid dissolution) and 0.92585 (solid acid saccharification) indicated the good fit of the models. An optimum reducing sugars yield of 99.0% at dissolution conditions: 1.5 h, 155 °C and 1.5% substrate loading; and saccharification conditions: 1.5 h, 120 °C and 6% catalyst loading, was achieved. Conclusion This study has demonstrated the good fit of a model for the sequential ionic liquid dissolution–solid acid saccharification process. The model developed has the potential to be used in predicting sugars production in the sequential ionic liquid dissolution–solid acid saccharification. © 2018 Society of Chemical Industry

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“…Different categories and application of cuckoo search algorithm are discussed in [9]. CS algorithm is found to outperform Differential Evolution algorithm with respect to convergence speed and efficiency of computation [10][11]. An extended binary version of the Cuckoo Search, namely BCS, is being used for mapping continuous solutions produced by CS to binary ones while performing feature selection.…”

Section: Related Workmentioning

confidence: 99%

Enhanced crop yield prediction using Monte Carlo method and binary cuckoo search

Surianarayanan¹,

Palanivel²,

Mani³

2020

Malaya J. Mat

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The yield of crops is influenced by various factors such as weather conditions, soil characteristics, irrigation facility, solar radiation, fertilizer application, tillage, etc. Accurate prediction of crop yield is an important issue in agriculture as un-presented changes in yield will significantly influence food supply and market prices. Data pre-processing and selection of relevant features is an essential step while perform prediction using machine learning algorithms. In this work, Monte Carlo simulation for random selection of data and binary cuckoo search for relevant feature selection are used with an objective of enhancing the accuracy of prediction using multiple linear regression technique. Experimental results are discussed.

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Performance Comparison of Cuckoo Search and Differential Evolution Algorithm for Constrained Optimization

Cited by 24 publications

References 11 publications

RETRACTED: Constrained cuckoo search algorithm and Takagi-Sugeno fuzzy models for predictive control

RETRACTED: Constrained cuckoo search algorithm and Takagi-Sugeno fuzzy models for predictive control

Interval type‐2 fuzzy logic with Karmen‐Mendel algorithm for sequential ionic liquid dissolution–solid acid saccharification

Enhanced crop yield prediction using Monte Carlo method and binary cuckoo search

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