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.
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.
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.
Inhaltsverzeichnis
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.
Klappentext
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.
