Application of JAYA algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA

Abhishek, Kumar; Kumar, Vivek; Datta, Saurav; Mahapatra, Siba Sankar

doi:10.1007/s00366-016-0484-8

Cited by 65 publications

(29 citation statements)

References 50 publications

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“…As the name indicated, TLBO includes two main phases: the "Teacher Phase", where the model learns something from a teacher and the "Learner phase", where the model learns itself by interacting with other learners. The structure of TLBO can be divided in three main steps as the followings [67][68][69][70]…”

Section: Teaching-learning-based Optimization (Tlbo)mentioning

confidence: 99%

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Pham

Dao

et al. 2019

Applied Sciences

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Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

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Section: Teaching-learning-based Optimization (Tlbo)mentioning

confidence: 99%

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Pham

Dao

et al. 2019

Applied Sciences

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“…In [42,43], JAYA was used to solve di erent engineering optimization problems. In [44], JAYA was used for the optimization of an integral controller. In [45,46], an economic load dispatch optimization was validated using di erent version of JAYA.…”

Section: Jaya Algorithmmentioning

confidence: 99%

Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single‐ and Multi‐Loop Distribution Systems

et al. 2019

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To ensure a safe and trustworthy pattern in contradiction to the possible faults, a precise, reliable, and fast relaying strategy is of high importance in an electrical power system. These challenges give the impression of being more refined in multi-loop distribution systems. More recently, overcurrent relays (OCRs) have evolved as proficient counteragents for such cases. In this way, inaugurating an optimal protection coordination strategy is accepted as the primary precondition in guaranteeing the safe protection of the coordination strategy. This study is aimed at lessening the overall operational time of the main relays in order to reduce the power outages. The coordination problem is conducted by adjusting only one parameter, namely the time multiplier setting (TMS). In electrical power relaying coordination, the objective function to be minimized is the sum of the overall operational time of the main relays. In the prescribed work, the coordination of the OCRs in the single- and multi-loop distribution network is realized as an optimization issue. The optimization is accomplished by means of JAYA algorithm. The suggested technique depends on the idea that the result acquired for a certain issue ought to pass near the finest result and avert the worst result. This technique involves only the common control factors and does not involve specific control factors. JAYA is adopted to OCR problem and run 20 times with the same initial condition for each case study, and it has been realized that for every run, the JAYA algorithm converges to the global optimum values requiring less number of iterations and computational time. The results obtained from JAYA algorithm are compared with other evolutionary and up-to-date algorithms, and it was determined that JAYA outperforms the other techniques.

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“…Assume ( , , 1) and ( , , 2) are two positive numbers in the range of [0, 1] and generated at random. The modified candidate ( , , ) is defined as (10) represents that the candidate needs to move away from the worst candidate, noting the "−" symbol before ( , , 2) [33]. The updated candidate at iteration ( + 1) is written as…”

Section: Jaya Algorithmmentioning

confidence: 99%

Identification of Alcoholism Based on Wavelet Renyi Entropy and Three‐Segment Encoded Jaya Algorithm

Wang

Muhammad

et al. 2018

Complexity

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The alcohol use disorder (AUD) is an important brain disease, which could cause the damage and alteration of brain structure. The current diagnosis of AUD is mainly done manually by radiologists. This study proposes a novel computer-vision-based method for automatic detection of AUD based on wavelet Renyi entropy and three-segment encoded Jaya algorithm from MRI scans. The wavelet Renyi entropy is proposed to provide multiresolution and multiscale analysis of features, describe the complexity of the brain structure, and extract the distinctive features. Grid search method was used to select the optimal wavelet decomposition level and Renyi order. The classifier was constructed based on feedforward neural network and a three-segment encoded (TSE) Jaya algorithm providing parameter-free training of the weights, biases, and number of hidden neurons. We have conducted the experimental evaluation on 235 subjects (114 are AUDs and 121 healthy). -fold cross validation has been used to avoid overfitting and report out-of-sample errors. The results showed that the proposed method outperforms four state-of-the-art approaches in terms of accuracy. The proposed TSE-Jaya provides a better performance, compared to the conventional approaches including plain Jaya, multiobjective genetic algorithm, particle swarm optimization, bee colony optimization, modified ant colony system, and real-coded biogeography-based optimization.

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Application of JAYA algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA

Cited by 65 publications

References 50 publications

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single‐ and Multi‐Loop Distribution Systems

Identification of Alcoholism Based on Wavelet Renyi Entropy and Three‐Segment Encoded Jaya Algorithm

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