Swarm Intelligence Based Optimization [electronic resource] : Second International Conference, ICSIBO 2016, Mulhouse, France, June 13-14, 2016, Revised Selected Papers / edited by Patrick Siarry, Lhassane Idoumghar, Julien Lepagnot.

Contributor(s): Siarry, Patrick [editor.] | Idoumghar, Lhassane [editor.] | Lepagnot, Julien [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 10103Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: IX, 125 p. 56 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319503073Subject(s): Computer science | Microprocessors | Special purpose computers | Architecture, Computer | Algorithms | Computer science -- Mathematics | Artificial intelligence | Computer Science | Algorithm Analysis and Problem Complexity | Artificial Intelligence (incl. Robotics) | Computer System Implementation | Special Purpose and Application-Based Systems | Processor Architectures | Discrete Mathematics in Computer ScienceAdditional physical formats: Printed edition:: No titleDDC classification: 005.1 LOC classification: QA76.9.A43Online resources: Click here to access online
Contents:
Theoretical advances of swarm intelligence metaheuristics -- Combinatorial discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large scale optimization -- Artificial immune systems, particle swarms, ant colony, bacterial forging, artificial bees, fireflies algorithm -- Hybridization of algorithms -- Parallel/distributed computing, machine learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles -- Adaptation and applications of swarm intelligence principles to real world problems in various domains.
In: Springer eBooksSummary: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Swarm Intelligence Based Optimization, ICSIBO 2016, held in Mulhouse, France, in June 2016. The 9 full papers presented were carefully reviewed and selected from 20 submissions. They are centered around the following topics: theoretical advances of swarm intelligence metaheuristics; combinatorial discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large scale optimization; artificial immune systems, particle swarms, ant colony, bacterial forging, artificial bees, fireflies algorithm; hybridization of algorithms; parallel/distributed computing, machine learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles; adaptation and applications of swarm intelligence principles to real world problems in various domains.
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Theoretical advances of swarm intelligence metaheuristics -- Combinatorial discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large scale optimization -- Artificial immune systems, particle swarms, ant colony, bacterial forging, artificial bees, fireflies algorithm -- Hybridization of algorithms -- Parallel/distributed computing, machine learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles -- Adaptation and applications of swarm intelligence principles to real world problems in various domains.

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Swarm Intelligence Based Optimization, ICSIBO 2016, held in Mulhouse, France, in June 2016. The 9 full papers presented were carefully reviewed and selected from 20 submissions. They are centered around the following topics: theoretical advances of swarm intelligence metaheuristics; combinatorial discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large scale optimization; artificial immune systems, particle swarms, ant colony, bacterial forging, artificial bees, fireflies algorithm; hybridization of algorithms; parallel/distributed computing, machine learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles; adaptation and applications of swarm intelligence principles to real world problems in various domains.

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