Particle swarm optimization: An overview

Particle swarm optimization algorithm: An overview

Wang, D., Tan, D., & Liu, L. (2018) · Soft Computing · doi:10.1007/s00500-016-2474-6 · Cited by 3,343 · see citing works

Self-Driving Laboratories for Chemistry and Materials Science

Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707 · see citing works

Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review

Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656 · see citing works

Stock market prediction using machine learning classifiers and social media, news

Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01839-w · Cited by 336 · see citing works

Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection

Pham, T. N., Tran, L. V., & Dao, S. V. T. (2020) · IEEE Access · doi:10.1109/access.2020.3031914 · Cited by 262 · see citing works

Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting

Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2020) · Energies · doi:10.3390/en13020391 · Cited by 256 · see citing works

An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries

Long, B., Xian, W., Jiang, L., & Liu, Z. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.01.006 · Cited by 244 · see citing works

The accuracy and efficiency of GA and PSO optimization schemes on estimating reaction kinetic parameters of biomass pyrolysis

Ding, Y., Zhang, W., Yu, L., & Lu, K. (2019) · Energy · doi:10.1016/j.energy.2019.04.030 · Cited by 220 · see citing works

Decision Making in Multiagent Systems: A Survey

Rizk, Y., Awad, M., & Tunstel, E. W. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2840971 · Cited by 219 · see citing works

A novel hybrid GWO-SCA approach for optimization problems

Singh, N., & Singh, S. B. (2017) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2017.11.001 · Cited by 214 · see citing works

An integrated planning framework for different types of PEV charging facilities in urban area

Zhang, H., Hu, Z., Xu, Z., & Song, Y. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2436069 · Cited by 209 · see citing works

A review on nonlinear energy sinks: designs, analysis and applications of impact and rotary types

Saeed, A. S., Abdul Nasar, R., & AL-Shudeifat, M. A. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-022-08094-y · Cited by 208 · see citing works

Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm

Biswas, P. P., Suganthan, P. N., Wu, G., & Amaratunga, G. A. J. (2019) · Renewable Energy · doi:10.1016/j.renene.2018.07.152 · Cited by 190 · see citing works

Time cells in hippocampal area CA3

Salz, D. M., Tiganj, Z., Khasnabish, S., Kohley, A., Sheehan, D., Howard, M. W., & Eichenbaum, H. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0087-16.2016 · Cited by 183 · see citing works

Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism

Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R.-A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.04.002 · Cited by 183 · see citing works

Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO–SVR model

Qin, T., Zeng, S., & Guo, J. (2015) · Microelectronics Reliability · doi:10.1016/j.microrel.2015.06.133 · Cited by 176 · see citing works

Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders

Raghavendra, U., Acharya, U. R., & Adeli, H. (2019) · European Neurology · doi:10.1159/000504292 · Cited by 174 · see citing works

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

Beyeler, M., Nanduri, D., Weiland, J. D., Rokem, A., Boynton, G. M., & Fine, I. (2019) · Scientific Reports · doi:10.1038/s41598-019-45416-4 · Cited by 143 · see citing works

Performance prediction and analysis of a PEM fuel cell operating on pure oxygen using data-driven models: A comparison of artificial neural network and support vector machine

Han, I.-S., & Chung, C.-B. (2016) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2016.04.247 · Cited by 141 · see citing works

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23063293 · Cited by 146 · see citing works

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Ali, A., & Söffker, D. (2018) · Energies · doi:10.3390/en11030476 · Cited by 134 · see citing works

An efficient power scheduling scheme for residential load management in smart homes

Rasheed, M., Javaid, N., Ahmad, A., Khan, Z., Qasim, U., & Alrajeh, N. (2015) · Applied Sciences · doi:10.3390/app5041134 · Cited by 134 · see citing works

Support vector machine for diagnosis Cancer disease: a comparative study

Sweilam, N. H., Tharwat, A. A., & Abdel Moniem, N. K. (2010) · Egyptian Informatics Journal · doi:10.1016/j.eij.2010.10.005 · Cited by 131 · see citing works

Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR)

Khan, P. M., & Roy, K. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1542428 · Cited by 113 · see citing works

Embedded Intelligence on FPGA: Survey, Applications and Challenges

Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105 · see citing works

A review on the self and dual interactions between machine learning and optimisation

Song, H., Triguero, I., & Özcan, E. (2019) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00185-z · Cited by 101 · see citing works

M-AdaBoost-A based ensemble system for network intrusion detection

Zhou, Y., Mazzuchi, T. A., & Sarkani, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113864 · Cited by 99 · see citing works

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

Zhao, L., Zhao, W., Hawbani, A., Al-Dubai, A. Y., Min, G., Zomaya, A. Y., & Gong, C. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3046275 · Cited by 95 · see citing works

Application of cuckoo search algorithm to constrained control problem of a parallel robot platform

Stojanovic, V., Nedic, N., Prsic, D., Dubonjic, L., & Djordjevic, V. (2016) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-016-8627-z · Cited by 94 · see citing works

A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine

Zhong, W., Zhuang, Y., Sun, J., & Gu, J. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1194-2 · Cited by 83 · see citing works

A multiple combined method for rebalancing medical data with class imbalances

Wang, Y.-C., & Cheng, C.-H. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104527 · Cited by 66 · see citing works

Meta-heuristic as manager in federated learning approaches for image processing purposes

Połap, D., & Woźniak, M. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107872 · Cited by 65 · see citing works

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

Guo, B., Zhuang, Z., Pan, J.-S., & Chu, S.-C. (2021) · IEEE Access · doi:10.1109/access.2021.3049421 · Cited by 59 · see citing works

COVID-19 optimizer algorithm, modeling and controlling of coronavirus distribution process

Hosseini, E., Ghafoor, K. Z., Sadiq, A. S., Guizani, M., & Emrouznejad, A. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3012487 · Cited by 55 · see citing works

LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning

Jeon, B.-K., & Kim, E.-J. (2021) · Sustainability · doi:10.3390/su13020894 · Cited by 55 · see citing works

Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers

Shi, T., Ma, H., & Chen, G. (2018) · 2018 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2018.8477708 · Cited by 53 · see citing works

A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system

Chang, T.-Y., & Ke, Y.-R. (2013) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.04.002 · Cited by 40 · see citing works

Surfing Multiple Conformation-Property Landscapes via Machine Learning: Designing Single-Ion Magnetic Anisotropy

Lunghi, A., & Sanvito, S. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c01187 · Cited by 35 · see citing works

Sample Subset Optimization for Classifying Imbalanced Biological Data

Yang, P., Zhang, Z., Zhou, B. B., & Zomaya, A. Y. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-20847-8_28 · Cited by 31 · see citing works

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Hassine, H., Barkallah, M., & Bellacicco, A. (2015) · International Journal of Simulation Modelling · doi:10.2507/ijsimm14(1)9.292 · Cited by 28 · see citing works

A review of computational intelligence techniques in coral reef-related applications

Salcedo-Sanz, S., Cuadra, L., & Vermeij, M. J. A. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.01.008 · Cited by 27 · see citing works

ModelCoder: A Fault Model based Automatic Root Cause Localization Framework for Microservice Systems

Cai, Y., Han, B., Li, J., Zhao, N., & Su, J. (2021) · 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) · doi:10.1109/iwqos52092.2021.9521318 · Cited by 14 · see citing works

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

Beyeler, M., Nanduri, D., Weiland, J. D., Rokem, A., Boynton, G. M., & Fine, I. (2018) · doi:10.1101/453035 · Cited by 13 · see citing works

Improved Beetle Antennae Search Algorithm-Based Lévy Flight for Tuning of PID Controller in Force Control System

Fan, Y., Shao, J., Sun, G., & Shao, X. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/4287315 · Cited by 18 · see citing works

Evolutionary Algorithms for Wireless Communications — A Review of the State-of-the art

Goudos, S. K. (2014) · Contemporary Issues in Wireless Communications · doi:10.5772/59147 · Cited by 13 · see citing works

Rogue access point localization using particle swarm optimization

Awad, F., Al-Refai, M., & Al-Qerem, A. (2017) · 2017 8th International Conference on Information and Communication Systems (ICICS) · doi:10.1109/iacs.2017.7921985 · Cited by 13 · see citing works

A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs

Li, D., Huang, L., Wang, K., Pang, W., Zhou, Y., & Zhang, R. (2018) · IEEE Access · doi:10.1109/access.2018.2882455 · Cited by 11 · see citing works