Advances in Swarm Intelligence, 1st ed. 2018 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I Theoretical Computer Science and General Issues Series
Coordonnateurs : Tan Ying, Shi Yuhui, Tang Qirong
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 as follows: theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithms; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization; multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; prediction, 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 algorithms.- bacterial foraging optimization.- artificial immune system.- hydrologic cycle optimization.- other swarm-based optimization algorithms.- hybrid optimization algorithms.- multi-objective optimization.- large-scale global optimization.- multi-agent systems.- swarm robotics; fuzzy logic approaches.- planning and routing problems.- recommendation in social media.- prediction.- classification.- finding patterns.- image enhancement.- deep learning.
Date de parution : 06-2018
Ouvrage de 639 p.
15.5x23.5 cm
Thèmes d’Advances in Swarm Intelligence :
Mots-clés :
Computer Science; artificial intelligence; clustering; differential evolution; evolutionary algorithms; genetic algorithms; hybrid optimization algorithms; learning algorithms; local optima; multi-agent systems; multiobjective optimization; pareto principle; particle swarm optimization (pso); problem solving; sensors; signal processing; software engineering; swarm intelligence; swarm robotics; wireless sensor networks