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008			190603s2019 gw a s |||| 0|eng d
020			_a9783030150709
024	7		_a10.1007/978-3-030-15070-9 _2doi
040			_aES-MaUEC _bspa _dES-MaUEC
050		4	_aQ337.3 _b2019 EB
245	0	0	_aBrain Storm Optimization Algorithms : _bConcepts, Principles and Applications _cedited by Shi Cheng, Yuhui Shi
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (XV, 299 páginas) _b108 ilustraciones,58 ilustraciones a color
347			_atext file _bPDF
490	0		_aAdaptation Learning and Optimization _x1867-4534 _v23
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aBrain Storm Optimization Algorithms: More Questions than Answers -- Brain Storm Optimization for Test Task Scheduling Problem -- Oppositional Brain Storm Optimization for Fault Section Location in Distribution Networks -- Multi-objective Brain Storm Optimization Based on Differential Evolution for Environmental/Economic Dispatch Problem -- Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation -- Brain Storm Algorithm Combined with Covariance Matrix Adaptation Evolution Strategy for Optimization -- A Feature Extraction Method Based on BSO Algorithm for Flight Data -- Brain Storm Optimization Algorithms for Solving Equations Systems -- StormOptimus: A Single Objective Constrained Optimizer Based on Brainstorming Process for VLSI Circuits -- Brain Storm Optimization Algorithms for Flexible Job Shop Scheduling Problem -- Enhancement of Voltage Stability using FACTS Devices in Electrical Transmission System with Optimal Rescheduling of Generators by Brain Storm Optimization Algorithm.
520	3		_aBrain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process. Since the introduction of BSO algorithms in 2011, many studies on them have been conducted. They not only offer an optimization method, but could also be viewed as a framework of optimization techniques. The process employed in the algorithms could be simplified as a framework with two basic operations: the converging operation and the diverging operation. A "good enough" optimum could be obtained through recursive solution divergence and convergence. The resulting optimization algorithm would naturally have the capability of both convergence and divergence. This book is primarily intended for researchers, engineers, and graduate students with an interest in BSO algorithms and their applications. The chapters cover various aspects of BSO algorithms, and collectively provide broad insights into what these algorithms have to offer. The book is ideally suited as a graduate-level textbook, whereby students may be tasked with the study of the rich variants of BSO algorithms that involves a hands-on implementation to demonstrate the utility and applicability of BSO algorithms in solving optimization problems.
650		7	_aInteligencia artificial distribuida _2embne _9666577
700	1		_aCheng, Shi _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aShi, Yuhui _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service) _9106996
776	0	8	_iPrinted edition: _z9783030150693
776	0	8	_iPrinted edition: _z9783030150716
776	0	8	_iPrinted edition: _z9783030150723
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-15070-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _a_alco _a_vill _b11/2019 _cm _dz _ea _feng _ggw _h0