Advances in Swarm Intelligence: 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part II (Lecture Notes in Computer Science, nr. 10942)

Editat de Ying Tan, Yuhui Shi, Qirong Tang
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en Limba Engleză Paperback – 16 Jun 2018
The two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections namely: multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; predication; classification; finding patterns; image enhancement; deep learning; theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization.
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ISBN-13: 9783319938172
ISBN-10: 3319938177
Pagini: 554
Ilustrații: XXIV, 579 p. 247 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.9 kg
Ediția: 1st ed. 2018
Editura: Springer
Colecția Springer
Seriile Lecture Notes in Computer Science , Theoretical Computer Science and General Issues

Locul publicării: Cham, Switzerland


Multi-agent systems.- swarm robotics.- fuzzy logic approaches.- planning and routing problems.- recommendation in social media.- predication.- classification.- finding patterns.- image enhancement.- deep learning.- theories and models of swarm intelligence.- ant colony optimization.- particle swarm optimization.- artificial bee colony algorithms.- genetic algorithms.- differential evolution.- fireworks algorithm.- bacterial foraging optimization.- artificial immune system.- hydrologic cycle optimization.- other swarm-based optimization algorithms.- hybrid optimization algorithms.- multi-objective optimization.- large-scale global optimization.