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001435243 020__ $$a9789813361041$$q(electronic bk.)
001435243 020__ $$a9813361042$$q(electronic bk.)
001435243 020__ $$z9789813361034
001435243 0247_ $$a10.1007/978-981-33-6104-1$$2doi
001435243 035__ $$aSP(OCoLC)1243551879
001435243 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dOCLCO$$dEBLCP$$dOCLCF$$dUKAHL$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ
001435243 049__ $$aISEA
001435243 050_4 $$aQA76.9.A43
001435243 08204 $$a005.13$$223
001435243 24500 $$aApplications of flower pollination algorithm and its variants /$$cNilanjan Dey, editors.
001435243 260__ $$aSingapore :$$bSpringer,$$c2021.
001435243 300__ $$a1 online resource (247 pages)
001435243 336__ $$atext$$btxt$$2rdacontent
001435243 337__ $$acomputer$$bc$$2rdamedia
001435243 338__ $$aonline resource$$bcr$$2rdacarrier
001435243 4901_ $$aSpringer tracts in nature-inspired computing
001435243 500__ $$a4 Numerical Experiments and Results.
001435243 5050_ $$aIntro -- Preface -- Contents -- Editor and Contributors -- 1 Flower Pollination Algorithm: Basic Concepts, Variants, and Applications -- 1 Introduction -- 2 Biological Inspirations: Pollination of Flowering Plants -- 3 Flower Pollination Optimization Algorithm (FPA) -- 3.1 Global Search in FPA: Biotic Pollination Process -- 3.2 Local Search in FPA: Abiotic Pollination Process -- 3.3 Switch Probability in FPA -- 3.4 Parametric Study for FPA -- 3.5 Implementation of FPA -- 3.6 Advantages of FPA -- 4 Variants of FPA -- 4.1 Multi-objective Flower Pollination Algorithm (MOFPA)
001435243 5058_ $$a4.2 Modified Flower Pollination Algorithms (M-FPA) -- 4.3 Hybridized Variants of FPA -- 5 Applications of FPA and Its Variants -- 6 Comparative Analytical Studies of FPA and its Variants -- 7 Limitations of FPA -- 8 Challenging Problems in FPA -- 9 Conclusions -- References -- 2 Optimization of Non-rigid Demons Registration Using Flower Pollination Algorithm -- 1 Introduction -- 2 Methodology -- 2.1 Demons Registration -- 2.2 Flower Pollination Algorithm -- 3 Proposed Method -- 4 Results and Discussion -- 5 Conclusion -- References
001435243 5058_ $$a3 Adaptive Neighbor Heuristics Flower Pollination Algorithm Strategy for Sequence Test Generation -- 1 Introduction -- 2 T-way Tests Generation Problem -- 2.1 T-way Tests Generation -- 2.2 Sequence t-way Tests Generation -- 3 Related Works -- 4 Adaptive Neighbor Heuristics Flower Pollination Algorithm Strategy -- 5 Experimental Results -- 5.1 Benchmarking with Existing Strategies -- 5.2 Convergence Rate Analysis -- 6 Summary -- References -- 4 Implementation of Flower Pollination Algorithm to the Design Optimization of Planar Antennas -- 1 Introduction -- 2 Flower Pollination Algorithm
001435243 5058_ $$a2.1 Pollination Phenomenon -- 2.2 Modeling of Flower Pollination Algorithm -- 3 The Cooperating Platform for Simulation and Optimization of the Antenna Designs -- 3.1 The Cooperating Platform -- 3.2 S-parameters -- 3.3 Cooperation of FPA and the Simulator -- 4 The Optimized Designs of Planar Antennas -- 4.1 UWB Antenna Design -- 4.2 Dual BN Characteristic Optimization of the UWB Antenna -- 4.3 Single UWB Antenna Element for a Quad-Element MIMO Antenna -- 4.4 Quad-Element MIMO Antenna -- 5 Conclusions -- References -- 5 Flower Pollination Algorithm for Slope Stability Analysis -- 1 Introduction
001435243 5058_ $$a2 Problem Statement -- 2.1 Generation of Trial Slip Surface -- 2.2 Calculation of Factor of Safety -- 2.3 Application of Optimization Method -- 3 Flower Pollination Algorithm -- 4 Numerical Analysis -- 4.1 Sensitivity Analysis -- 4.2 Case-1 -- 4.3 Case-2 -- 4.4 Case-3 -- 5 Discussion and Conclusions -- References -- 6 Optimum Sizing of Truss Structures Using a Hybrid Flower Pollinations -- 1 Introduction -- 2 Sizing Optimization Problem -- 3 Optimization Algorithms -- 3.1 Flower Pollination Algorithm -- 3.2 Differential Evolution -- 3.3 Hybrid Flower Pollination-Differential Evolution
001435243 506__ $$aAccess limited to authorized users.
001435243 520__ $$aThis book presents essential concepts of traditional Flower Pollination Algorithm (FPA) and its recent variants and also its application to find optimal solution for a variety of real-world engineering and medical problems. Swarm intelligence-based meta-heuristic algorithms are extensively implemented to solve a variety of real-world optimization problems due to its adaptability and robustness. FPA is one of the most successful swarm intelligence procedures developed in 2012 and extensively used in various optimization tasks for more than a decade. The mathematical model of FPA is quite straightforward and easy to understand and enhance, compared to other swarm approaches. Hence, FPA has attracted attention of researchers, who are working to find the optimal solutions in variety of domains, such as N-dimensional numerical optimization, constrained/unconstrained optimization, and linear/nonlinear optimization problems. Along with the traditional bat algorithm, the enhanced versions of FPA are also considered to solve a variety of optimization problems in science, engineering, and medical applications.
001435243 650_0 $$aComputer algorithms.
001435243 650_0 $$aSwarm intelligence.
001435243 650_6 $$aAlgorithmes.
001435243 655_0 $$aElectronic books.
001435243 7001_ $$aDey, Nilanjan,$$d1984-$$eeditor.
001435243 77608 $$iPrint version:$$aDey, Nilanjan.$$tApplications of Flower Pollination Algorithm and Its Variants.$$dSingapore : Springer Singapore Pte. Limited, ©2021$$z9789813361034
001435243 830_0 $$aSpringer tracts in nature-inspired computing.
001435243 852__ $$bebk
001435243 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-6104-1$$zOnline Access$$91397441.1
001435243 909CO $$ooai:library.usi.edu:1435243$$pGLOBAL_SET
001435243 980__ $$aBIB
001435243 980__ $$aEBOOK
001435243 982__ $$aEbook
001435243 983__ $$aOnline
001435243 994__ $$a92$$bISE