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Data Analysis using Particle Swarm Optimization Algorithm by Tai, Zu Jie

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Clustering chemical data set using particle swarm optimization based algorithm by Triyono, Triyono

“…In this study, Particle Swarm Optimization (PSO) based clustering algorithm is exploited to optimize the results of other clustering algorithm such as K-means. …”
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Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models by Gholami M., Ghanbari-Adivi E., Ehteram M., Singh V.P., Najah Ahmed A., Mosavi A., El-Shafie A.

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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization by Zuwairie, Ibrahim, Mohamad Nizam, Aliman, Fardila, Naim, Sophan Wahyudi, Nawawi, Shahdan, Sudin

“…Particle Swarm Optimization (PSO) and Gravitational Search Algorithm are a well known population-based heuristic optimization techniques. …”
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An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions by Mannan M., Roslan M.F., Reza M.S., Mansor M., Jern K.P., Hossain M.J., Hannan M.A.

“…This study presents an optimal schedule controller for microgrid energy management, utilizing the Binary Particle Swarm algorithm (BPSO) to minimize costs and ensure optimal power delivery to loads. …”

Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm by Ehteram M., Othman F.B., Yaseen Z.M., Afan H.A., Allawi M.F., Malek M.B.A., Ahmed A.N., Shahid S., Singh V.P., El-Shafie A.

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection by Tubishat, Mohammad, Idris, Norisma, Shuib, Liyana, Abushariah, Mohammad A.M., Mirjalili, Seyedali

“…In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. …”
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Advances in Particle Swarm Algorithms in Asynchronous, Discrete and Multi-Objective Optimization by Zuwairie, Ibrahim

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Evaluation of data mining models for predicting concrete strength by Wong, Chuan Ming

“…The Particle Swarm Optimization algorithm is able to generate optimal values for the concrete features that maximizes the strength of concrete. …”
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An improved particle swarm optimization algorithm for data classification by Waqas Haider Bangyal, Kashif Nisar, Tariq Rahim Soomro, Ag Asri Ag Ibrahim, Ghulam Ali Mallah, Nafees Ul Hassan, Najeeb Ur Rehman

“…Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. …”
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Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm by Aljanad A., Tan N.M.L., Agelidis V.G., Shareef H.

A salp swarm algorithm to improve power production of wind plant by Ahmad Zairi, Mohd Zain, Mohd Ashraf, Ahmad

“…Currently, the main problem of wind lantpowerproduction is definitely the control system of a wind generator that is not able to cope with the impact of turbulence and thus weakens complete power output.In this papera Salp Swarm Algorithm (SSA) is proposed as a data-drivenmethod to improve the controller variable and thus optimize the complete power production of the wind plant.The SSA is among of the meta-heuristic technique and imitates the salps chain's swarm movement depending on the food placement. …”
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Feature selection based on particle swarm optimization algorithm for sentiment analysis classification by Nurcahyawati, Vivine, Mustaffa, Zuriani

“…An improved approach was proposed to increase the sentiment analysis accuracy based on text pre-processing and Naïve Bayes Classifier algorithm hybrid with Particle Swarm Optimization (NBC-PSO). …”
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Automatic Multi-Objective Clustering Algorithm Using Hybrid Particle Swarm Optimization With Simulated Annealing. by Abubaker, Ahmad Asad

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An Improved Hybrid of Particle Swarm Optimization and the Gravitational Search Algorithm to Produce a Kinetic Parameter Estimation of Aspartate Biochemical Pathways by Ahmad Muhaimin, Ismail, Mohd Saberi, Mohamad, Hairudin, Abdul Majid, Khairul Hamimah, Abas, Safaai, Deris, Zaki, Nazar, Siti Zaiton, Mohd Hashim, Zuwairie, Ibrahim, Muhammad Akmal, Remli

“…The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. …”
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Two objectives big data task scheduling using swarm intelligence in cloud computing by Diallo, Laouratou, Hassan Abdalla Hashim, Aisha, Olanrewaju, Rashidah Funke, Islam, Shayla, Zarir, Abdullah Ahmad

“…Then, it proposes a type of swarm intelligence called Particle Swarm Optimization (PSO) algorithm to diminish cost though meeting deadlines. …”
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An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli by Mohammed Adam, Kunna, Tuty Asmawaty, Abdul Kadir, Muhammad Akmal, Remli, Noorlin, Mohd Ali, Kohbalan, Moorthy, Noryanti, Muhammad

“…In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. …”
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Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters by Yousri D., Thanikanti S.B., Allam D., Ramachandaramurthy V.K., Eteiba M.B.

Metaheuristic Algorithms and Neural Networks in Hydrology

“…It starts with the introduction of ANNs as a black box model, followed by the coupling of various metaheuristic algorithms with ANNs to form novel neural network models for solving real-world problems in hydrology, including Particle Swarm Optimization (PSO) for rainfall-runoff modeling, Bat Optimization (Bat) and Cuckoo Search Optimization (CSO) for future rainfall prediction, the Whale Optimization Algorithm (WOA) and Salp Swarm Optimization (SSO) for future water level prediction, Grey Wolf Optimization (GWO), Multi-Verse Optimization (MVO), the Sine Cosine Algorithm (SCA) and the Hybrid Sine Cosine and Fitness Dependent Optimizer (SC-FDO) for imputing missing rainfall data.…”
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