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Recent advances in swarm intelligence and evolutionary computation

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristi...

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Autor principal: Yang, Xin-She
Lenguaje:eng
Publicado: Springer 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-13826-8
http://cds.cern.ch/record/1980534
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author Yang, Xin-She
author_facet Yang, Xin-She
author_sort Yang, Xin-She
collection CERN
description This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.
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spelling cern-19805342021-04-21T20:38:23Zdoi:10.1007/978-3-319-13826-8http://cds.cern.ch/record/1980534engYang, Xin-SheRecent advances in swarm intelligence and evolutionary computationEngineeringThis timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.Springeroai:cds.cern.ch:19805342015
spellingShingle Engineering
Yang, Xin-She
Recent advances in swarm intelligence and evolutionary computation
title Recent advances in swarm intelligence and evolutionary computation
title_full Recent advances in swarm intelligence and evolutionary computation
title_fullStr Recent advances in swarm intelligence and evolutionary computation
title_full_unstemmed Recent advances in swarm intelligence and evolutionary computation
title_short Recent advances in swarm intelligence and evolutionary computation
title_sort recent advances in swarm intelligence and evolutionary computation
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-13826-8
http://cds.cern.ch/record/1980534
work_keys_str_mv AT yangxinshe recentadvancesinswarmintelligenceandevolutionarycomputation