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008			210317s2021 si | s |||| 0|eng d
020			_a9789813361041
024	7		_a10.1007/978-981-33-6104-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A43 _b2021 EB
245	1	0	_aApplications of Flower Pollination Algorithm and its Variants _cedited by Nilanjan Dey.
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XI, 239 páginas) _b94 ilustraciones, 40 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSpringer Tracts in Nature-Inspired Computing _x2524-5538
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aFlower Pollination Algorithm: Basic Concepts, Variants and Applications -- Optimization of Non-rigid Demons Registration using Flower Pollination Algorithm -- Adaptive Neighbour Heuristics Flower Pollination Algorithm Strategy for Sequence Test Generation -- Implementation of flower pollination algorithm to the design optimization of planar antennas -- Flower Pollination Algorithm for Slope Stability Analysis -- Optimum Sizing of Truss Structures Using A Hybrid Flower Pollination -- Optimizing Reinforced Cantilever Retaining Walls Under Dynamic Loading Using Improved Flower Pollination Algorithm -- Multi-Objective Flower Pollination Algorithm and its Variants to Find Optimal Golomb Rulers for WDM System -- Applications of Flower Pollination algorithm in Wireless Sensor Networking and Image processing: A detailed study -- Flower pollination algorithm tuned PID controller for multi-source interconnected multi area power system.
520	3		_aThis book presents essential concepts of traditional Flower Pollination Algorithm (FPA) and its recent variants and also its application to find optimal solution for a variety of real-world engineering and medical problems. Swarm intelligence-based meta-heuristic algorithms are extensively implemented to solve a variety of real-world optimization problems due to its adaptability and robustness. FPA is one of the most successful swarm intelligence procedures developed in 2012 and extensively used in various optimization tasks for more than a decade. The mathematical model of FPA is quite straightforward and easy to understand and enhance, compared to other swarm approaches. Hence, FPA has attracted attention of researchers, who are working to find the optimal solutions in variety of domains, such as N-dimensional numerical optimization, constrained/unconstrained optimization, and linear/nonlinear optimization problems. Along with the traditional bat algorithm, the enhanced versions of FPA are also considered to solve a variety of optimization problems in science, engineering, and medical applications.
988			_aSpringer_Robotics_2021
650		7	_2embne _9141162 _aAlgoritmos
700	1		_aDey, Nilanjan, _d1984- _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _998032
776	0	8	_iPrinted edition: _z9789813361034
776	0	8	_iPrinted edition: _z9789813361058
776	0	8	_iPrinted edition: _z9789813361065
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-6104-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE