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A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting by Mohammed, Athraa Jasim, Ghathwan, Khalil Ibrahim, Yusof, Yuhanis

“…Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. …”
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Short term forecasting based on hybrid least squares support vector machines by Zuriani, Mustaffa, M. H., Sulaiman, Ernawan, Ferda, Noorhuzaimi, Mohd Noor

“…This study assesses the performance of each hybrid algorithms based on three statistical indices viz. Mean Square Error (MSE), Root Mean Square Percentage Error (RMSPE) and Theil’s U which is realized on raw and normalized data set. …”
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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

“…The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. …”
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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms by Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.

“…The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. …”

An AN-GA controlled SEPIC converter for photovoltaic grid integration by Priyadarshi N., Padmanaban S., Holm-Nielsen J.B., Ramachandaramurthy V.K., Bhaskar M.S.

Energy band gap modeling of doped bismuth ferrite multifunctional material using gravitational search algorithm optimized support vector regression by Owolabi, Taoreed O., Abd Rahman, Mohd Amiruddin

“…The energy band gap of doped bismuth ferrite is modeled in this contribution through the fusion of a support vector regression (SVR) algorithm with a gravitational search algorithm (GSA) using crystal lattice distortion as a predictor. …”
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A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization by Ehteram M., Sammen S.S., Panahi F., Sidek L.M.

A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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Quantum-Behaved Lightning Search Algorithm to Improve Indirect Field-Oriented Fuzzy-PI Control for im Drive by Hannan M.A., Ali J.A., Mohamed A., Amirulddin U.A.U., Tan N.M.L., Uddin M.N.

A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation by Goh, Hui Hwang, An, Zhen, Zhang, Dongdong, Dai, Wei, Kurniawan, Tonni Agustiono, Goh, Kai Chen

“…Experiment 2, on the other hand, employs the complete discharging data of the battery to train the model for predicting the SOH of the remaining cycles of other batteries. The error evaluation metrics used are mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). …”
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A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation by Hui Hwang Goh, Hui Hwang Goh, Zhen An, Zhen An, Dongdong Zhang, Dongdong Zhang, Wei Dai, Wei Dai, Tonni Agustiono Kurniawan, Tonni Agustiono Kurniawan, Kai Chen Goh, Kai Chen Goh

“…Experiment 2, on the other hand, employs the complete discharging data of the battery to train the model for predicting the SOH of the remaining cycles of other batteries. The error evaluation metrics used are mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). …”
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Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz by Aziz, Muhammad Aidil Adha

“…In this thesis, Mean Absolute Percentage Error (MAPE) is used to quantify the performance of the prediction. …”
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Optimal short term load forecasting using LSSVM and improved BFOA considering Malaysia pandemic disrupted situation by Zaini, Farah Anishah

“…The IBFOA is proposed by modifying the chemotaxis process in BFOA using a Sine Cosine Algorithm (SCA), which improves the convergence speed and accuracy of the algorithm. …”
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A novel RSSI prediction using imperialist competition algorithm (ICA), radial basis function (RBF) and firefly algorithm (FFA) in wireless networks by Goudarzi, Shidrokh, Hassan, Wan Haslina, Hassan Abdalla Hashim, Aisha, Soleymani, Seyed Ahmad, Anisi, Mohammad Hossein, Zakaria, Omar M.

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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom, Sampe, Jahariah

“…The proposed chewing detection classifies the chewing activity with an overall accuracy of 96.4% using a medium Gaussian support vector machine (SVM). …”
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A novel hybrid metaheuristic algorithm for short term load forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

“…With respect to that matter, this study presents a hybrid Least Squares Support Vector Machines (LSSVM) with a rather new Swarm Intelligence (SI) algorithm namely Grey Wolf Optimizer (GWO). …”
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Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia by Zaini, Farah Anishah, Sulaima, Mohamad Fani, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

“…Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. …”
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Improved bacterial foraging optimization algorithm with machine learning driven short term electricity load forecasting: A case study in Peninsular Malaysia by Sulaima, Mohamad Fani, Zaini, Farah Anishah, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

“…Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short‑term daily electricity load in Peninsular Malaysia. …”
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Dengue outbreak prediction: hybrid meta-heuristic model by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Ernawan, Ferda, Yuhanis, Yusof, Mohamad Farhan, Mohamad Mohsin

“…Therefore, a good prediction model is vital to predict the dengue outbreak cases. By using monthly disease surveillance and meteorological data, the performance of the prediction model is guided by Mean Square Error (MSE) and Root Mean Square Percentage Error (RMSPE). …”
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Hybrid Metaheuristic Algorithm for Short Term Load Forecasting by Zuriani, Mustaffa, M. H., Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

“…With respect to that matter, this study presents a hybrid Least Squares Support Vector Machines (LSSVM) with a rather new Swarm Intelligence (SI) algorithm namely Grey Wolf Optimizer (GWO). …”
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