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[null,"1.","Record Nr.",null,"UNINA990005326280403321"]
[null,null,"Autore",null,"Maspero, Gaston <1846-1916>"]
[null,null,"Titolo",null,"Égypte / par G. Maspero"]
[null,null,"Pubbl/distr/stampa",null,"Paris, : Libr. Hachette, 1919"]
[null,null,"Edizione",null,"[2e ed.]"]
[null,null,"Descrizione fisica",null,"XII, 326 p., 4 tav. ; 18 cm"]
[null,null,"Collana",null,"Ars una : histoire gènèrale de l'art"]
[null,null,"Locazione",null,"FLFBC"]
[null,null,"Collocazione",null,"ARCH. BM Z 009"]
[null,null,"Lingua di pubblicazione",null,"Francese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910767581903321"]
[null,null,"Titolo",null,"Applications of flower pollination algorithm and its variants / / editor, Nilanjan Dey"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"981-336-104-2"]
[null,null,"Descrizione fisica",null,"1 online resource (247 pages) : illustrations"]
[null,null,"Collana",null,"Springer Tracts in Nature-Inspired Computing."]
[null,null,"Disciplina",null,"518.1"]
[null,null,"Soggetti",null,"Algorithms"]
[{"$nulls":4},"Swarm intelligence"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Algorismes computacionals"]
[{"$nulls":4},"Intel·ligència computacional"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- 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) -- 4.2 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 -- 3 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 -- "]
[{"$nulls":4},"3 Related Works -- 4 Adaptive Neighbor Heuristics Flower Pollination Algorithm Strategy -- 5 Experimental Results -- 5.1 Benchmarking with Existing Strategies -- 5.2 Convergence Rate Analysis -- 6 Summary -- References -- 4 Implementation of Flower Pollination Algorithm to the Design Optimization of Planar Antennas -- 1 Introduction -- 2 Flower Pollination Algorithm -- 2.1 Pollination Phenomenon -- 2.2 Modeling of Flower Pollination Algorithm -- 3 The Cooperating Platform for Simulation and Optimization of the Antenna Designs."]
[{"$nulls":4},"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 -- 2 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 -- 4 Numerical Experiments and Results -- 5 10-Bar Planar Truss -- 5.1 17-Bar Planar Truss -- 5.2 45-Bar Planar Truss -- 6 Conclusion -- References -- 7 Optimizing Reinforced Cantilever Retaining Walls Under Dynamic Loading Using Improved Flower Pollination Algorithm -- 1 Introduction -- 2 Design Steps of Reinforced Retaining Walls -- 2.1 Geometrical Design Variables -- 2.2 Geotechnical Stability of Reinforced Cantilever Retaining Walls -- 2.3 Structural Constraints for Reinforced Cantilever Retaining Walls -- 3 Optimum Design of Reinforced Concrete Cantilever Retaining Walls -- 3.1 Objective Function -- 4 Optimization Algorithms -- 4.1 Flower Pollination Algorithm -- 4.2 Improved Flower Pollination Algorithm -- 5 Numerical Experiments -- 5.1 Example 1 -- 5.2 Example 2 -- 6 Conclusions -- References."]
[{"$nulls":4},"8 Multi-objective Flower Pollination Algorithm and Its Variants to Find Optimal Golomb Rulers for WDM Systems -- 1 Introduction -- 2 Optimal Golomb Rulers (OGRs) -- 3 Multi-objective Flower Pollination Algorithm and Its Variants -- 3.1 Multi-objective Flower Pollination Algorithm -- 3.2 Variants of Multi-objective Flower Pollination Algorithm -- 4 Problem Formulation -- 5 Results and Discussion -- 5.1 Comparative Study of Flower Pollination-Inspired MOAs in Terms of the Ruler Length and Total Occupied Unequally Spaced Optical Channel Bandwidth -- 5.2 Comparative Study of Flower Pollination-Inspired MOAs in Terms of BEF -- 5.3 Comparative Study of Flower Pollination-Inspired MOAs in Terms of Computational CPU Time -- 5.4 Maximum Computation Complexity of Flower Pollination-Inspired MOAs in Terms of Big O Notation -- 5.5 Wilcoxon Rank-Sum Test of Flower Pollination-Inspired MOAs -- 6 Conclusions -- References -- 9 Applications of Flower Pollination Algorithm in Wireless Sensor Networking and Image Processing: A Detailed Study -- 1 Introduction -- 2 Swarm Intelligence Algorithm -- 2.1 Bat Algorithm -- 2.2 Firefly Algorithm -- 2.3 Particle Swarm Optimization -- 2.4 Artificial Bee Colony Algorithm -- 2.5 Cuckoo Search Algorithm -- 3 Flower Pollination Algorithm -- 3.1 Flower Pollination -- 3.2 The Flower Pollination Algorithm (FPA) -- 3.3 Variants of Flower Pollination "]
[{"$nulls":4},"Algorithm (FPA) -- 4 Wireless Sensor Networks -- 4.1 Impact of Flower Pollination Algorithm in Wireless Sensor Networking -- 5 Image Processing -- 5.1 Impact of Flower Pollination Algorithm in Image Processing -- 6 Discussion -- 7 Conclusion -- References -- 10 Flower Pollination Algorithm Tuned PID Controller for Multi-source Interconnected Multi-area Power System -- 1 Introduction -- 2 Power System Investigation -- 2.1 Proportional Integral Derivative (PID) Controller."]
[{"$nulls":4},"3 Flower Pollination Algorithm Tuned PID Controller -- 4 Result Analysis and Discussions -- 5 Conclusions -- References."]
[null,null,null,"Sommario/riassunto","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."]