A new optimizer using particle swarm theory

Particle swarm optimization

Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192 · see citing works

Adaptation in Natural and Artificial Systems

Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works

THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS

Fisher, R. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 16,063 · see citing works

Flocks, herds and schools: A distributed behavioral model

Reynolds, C. W. (1987) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/37401.37406 · Cited by 7,795 · see citing works

Handbook of Genetic Algorithms

Davis, L. D. (1991) · Cumulative Index of Computer Aided Architectural Design · Cited by 7,297 · see citing works

Flocks, herds and schools: A distributed behavioral model

Reynolds, C. W. (1987) · ACM SIGGRAPH Computer Graphics · doi:10.1145/37402.37406 · Cited by 5,148 · see citing works

Particle Systems—a Technique for Modeling a Class of Fuzzy Objects

Reeves, W. T. (1983) · ACM Transactions on Graphics · doi:10.1145/357318.357320 · Cited by 1,079 · see citing works

A stochastic nonlinear model for coordinated bird flocks

Heppner, F. L., & Grenander, U. (1990) · Medical Entomology and Zoology · Cited by 276 · see citing works

Swarms, Phase Transitions, and Collective IntelligencePreprint

Millonas, M. M. (1993) · arXiv (Cornell University) · doi:10.48550/arxiv.adap-org/9306002 · Cited by 273 · see citing works

A Coevolutionary Approach to Learning Sequential Decision Rules

Potter, M. A., Jong, K. A. D., & Grefenstette, J. J. (1995) · Cited by 143 · see citing works

Generalized Convergence Models for Tournament- and (mu, lambda)-Selection

Bäck, T. (1995) · international conference on Genetic algorithms · Cited by 54 · see citing works

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works

A discrete binary version of the particle swarm algorithm

Kennedy, J., & Eberhart, R. C. (2002) · 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation · doi:10.1109/icsmc.1997.637339 · Cited by 4,767 · see citing works

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503 · see citing works

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

Derivative-free optimization: a review of algorithms and comparison of software implementations

Rios, L. M., & Sahinidis, N. V. (2013) · Journal of Global Optimization · doi:10.1007/s10898-012-9951-y · Cited by 1,276 · see citing works

A Competitive Swarm Optimizer for Large Scale Optimization

Cheng, R., & Jin, Y. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2322602 · Cited by 1,052 · see citing works

A social learning particle swarm optimization algorithm for scalable optimization

Cheng, R., & Jin, Y. (2015) · Information Sciences · doi:10.1016/j.ins.2014.08.039 · Cited by 747 · see citing works

Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks

Amini, M. H., Moghaddam, M. P., & Karabasoglu, O. (2017) · Sustainable Cities and Society · doi:10.1016/j.scs.2016.10.006 · Cited by 353 · see citing works

An improved particle swarm optimization for feature selection

Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011) · Journal of Bionic Engineering · doi:10.1016/s1672-6529(11)60020-6 · Cited by 324 · see citing works

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311 · see citing works

Metaheuristic Algorithms in Modeling and Optimization

Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013) · Metaheuristic Applications in Structures and Infrastructures · doi:10.1016/b978-0-12-398364-0.00001-2 · Cited by 268 · 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

Machine learning for precise quantum measurement

Hentschel, A., & Sanders, B. C. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.063603 · Cited by 197 · see citing works

Binary biogeography-based optimization based SVM-RFE for feature selection

Albashish, D., Hammouri, A. I., Braik, M., Atwan, J., & Sahran, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107026 · Cited by 154 · see citing works

GWO: a review and applications

Negi, G., Kumar, A., Pant, S., & Ram, M. (2021) · International Journal of System Assurance Engineering and Management · doi:10.1007/s13198-020-00995-8 · Cited by 132 · see citing works

Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions

Liu, D., Li, L., Song, Y., Wu, L., & Peng, Y. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.02.046 · Cited by 126 · see citing works

Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks

Alsarhan, A., Alauthman, M., Alshdaifat, E., Al-Ghuwairi, A.-R., & Al-Dubai, A. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-02963-x · Cited by 123 · see citing works

Introduction to Optimization

Eren, Y., Küçükdemiral, İ. B., & Üstoğlu, İ. (2017) · Optimization in Renewable Energy Systems · doi:10.1016/b978-0-08-101041-9.00002-8 · Cited by 67 · see citing works

Swarm intelligence: new techniques for adaptive systems to provide learning support

Wong, L.-H., & Looi, C.-K. (2012) · Interactive Learning Environments · doi:10.1080/10494821003714681 · Cited by 50 · see citing works

Power System Anomaly Detection Based on Ocsvm Optimized by Improved Particle Swarm Optimization

Wang, Z., Fu, Y., Song, C., Zeng, P., & Qiao, L. (2019) · IEEE Access · doi:10.1109/access.2019.2959699 · Cited by 47 · see citing works

Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

Chun-Feng, W., Kui, L., & Pei-Ping, S. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/832949 · Cited by 38 · see citing works

Geometrical Optimization of the EHL Roller Face/Rib Contact for Energy Efficiency in Tapered Roller Bearings

Wirsching, S., Marian, M., Bartz, M., Stahl, T., & Wartzack, S. (2021) · Lubricants · doi:10.3390/lubricants9070067 · Cited by 25 · see citing works

Study the effects of ambient temperature on full-scale rubber bearings

Pang, H., Jiang, T., Dai, J., & Yang, Y. (2025) · Structures · doi:10.1016/j.istruc.2025.108535 · Cited by 8

Swarm-Bots and Swarmanoid: Two Experiments in Embodied Swarm Intelligence

Dorigo, M. (2009) · IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2009.377 · Cited by 6 · see citing works

Swarm Robotics: The Coordination of Robots via Swarm Intelligence Principles

Dorigo, M. (2008) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-0-387-09655-1_1 · Cited by 2 · see citing works

Swarm-Bots and Swarmanoid: Two Experiments in Embodied Swarm Intelligence

Dorigo, M. (2009) · IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2009.370 · Cited by 9 · see citing works

Special Issue on Design of Swarm Intelligence Through Interdisciplinary Approach

Kano, T., & SUEOKA, Y. (2023) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2023.p0889

Keynote speaker

Dorigo, M. (2007) · Bioinspired Models of Network, Information, and Computing Systems · doi:10.1109/bimnics.2007.4610064