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001			393866
003			ES-MaUEC
005			20230102123033.0
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008			220831s2022 sz | s |||| 0|eng d
020			_a9783031075124
024	7		_a10.1007/978-3-031-07512-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aHandbook of Nature-Inspired Optimization Algorithms: The State of the Art _bVolume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems _cedited by Ali Mohamed, Diego Oliva, Ponnuthurai Nagaratnam Suganthan
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (X, 279 páginas) _b94 ilustraciones, 73 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aStudies in Systems Decision and Control _x2198-4190 _v212
505	0		_aChaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow -- Design and Performance Evaluation of Objective Functions Based on Various Measures of Fuzzy Entropies for Image Segmentation using Grey Wolf Optimization -- Improved Artificial Bee Colony Algorithm with Adaptive Pursuit Based Strategy Selection -- Beetle Antennae Search Algorithm for the Motion Planning of Industrial Manipulator -- Solving Optimal Power Flow with Considering Placement of TCSC and FACTS Cost Using Cuckoo Search Algorithm.
520			_aThe introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving. The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.
700	1		_aMohamed, Ali _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aOliva, Diego _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSuganthan, Ponnuthurai Nagaratnam _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783031075117
776	0	8	_iPrinted edition: _z9783031075131
776	0	8	_iPrinted edition: _z9783031075148
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-07512-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Robotics_2022
999			_c393866 _d393866