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Applications of Flower Pollination Algorithm and its Variants / edited by Nilanjan Dey.

Contributor(s): Dey, Nilanjan, (1984-), editor literario
Series: (Springer Tracts in Nature-Inspired Computing, 2524-5538); (Intelligent Technologies and Robotics (SpringerNature-42732)); (Intelligent Technologies and Robotics (R0) (SpringerNature-43728)).Publisher: Singapore : Springer International Pulishing, 2021Edition: First edition 2021.Description: 1 recurso en línea (XI, 239 páginas) : 94 ilustraciones, 40 ilustraciones a color.ISBN: 9789813361041.Subject: AlgoritmosOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Flower 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.
Abstract: This 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería QA76.9.A43 2021 EB (Browse shelf(Opens below)) Acceso electrónico eBook.23122040
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Flower 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.

This 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.

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