Recent Advances in Swarm Intelligence Algorithms and Their Applications
Dong, Jian (editor)
This reprint comprises a collection of 20 articles that were accepted and published in the Special Issue titled " Recent Advances in Swarm Intelligence Algorithms and Their Applications" of the MDPI journal Mathematics. These articles cover a wide range of topics related to swarm intelligence algorithms and their theories. The topics include improvements in algorithm mechanisms, fusion algorithms, multiobjective optimization, and the optimization of large-scale problems, as well as the application of swarm intelligence in various fields such as engineering optimization problems, vehicle swarm motion, viscoelastic Maxwell-type DVA, and deep learning. It is hoped that this reprint will be helpful for scholars engaged in the field of swarm intelligence and also suitable for researchers interested in staying updated with the latest advancements in swarm intelligence algorithms and their applications. Nowadays, swarm intelligence is closely intertwined with various aspects of life. This reprint aims to provide readers with an understanding of these developments and further expand the application boundaries of such algorithms.
Keywordspreference incorporation; ant colony optimisation; grey wolf optimisation; interval outranking; multi-criteria decision analysis; swarm intelligence algorithms; piecewise linearization; optimization; parameter tuning; approximation; experimental comparison; graph convolutional network; relation extraction; machine learning; natural language processing; metaheuristic algorithms; Whale Optimization Algorithm; HBA; radial distribution systems; power loss; sensitivity analysis; DG optimal allocation; voltage deviation; capacitor banks; electricity market; optimal bidding; Harris Hawk Optimization; multi layered neural network; bi-level optimization; strategic bidding; ground-penetrating radar (GPR); cavity morphology recognition; few-shot learning (FSL); deep learning (DL); relation network (RelationNet); unmanned aerial vehicle swarm; antenna array; near-field beamforming; array element position error compensation; spatial area division; economic load dispatch; pigeon-inspired optimizer; oppositional-based learning; swarm intelligence algorithm; oppositional-based pigeon-inspired optimizer; tunicate swarm algorithm; chaotic mapping; Lévy flight strategy; benchmark test functions; engineering design problems; meta-heuristic; large-scale agents; attack and defense; multi-population mean-field game; high-dimensional solution space; neural networks; bat algorithm; hybrid strategy; energy harvesting; NOMA; cognitive relay network; multimodal optimization; multiple optima; partition-based random search; niching; global optimization problems; moth-flame optimization; premature convergence; population diversity; grey wolf optimization; swarm intelligence; real world application; spiking neural P system; AEC system; LMS; neuromorphic architecture; FPGA; Harris hawks optimization; elite opposition-based learning; Sobol sequence; nonlinear weight; Gaussian walk learning; particle swarm optimization algorithm; dynamic vibration absorber; Maxwell-type; inerter; negative stiffness; chaotic-based PPE algorithm; meta-heuristic algorithm; chaotic maps; Mahjong; differential evolution; deficiency number; combinatorial optimization; reinforcement learning; multi-agent reinforcement learning; self play; population play; n/a
Webshop linkhttps://mdpi.com/books/pdfview ...
Publication date and placeBasel, 2023
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