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001453203 001__ 1453203
001453203 003__ OCoLC
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001453203 019__ $$a1350185960
001453203 020__ $$a9783031201059$$q(electronic bk.)
001453203 020__ $$a3031201051$$q(electronic bk.)
001453203 020__ $$z3031201043
001453203 020__ $$z9783031201042
001453203 0247_ $$a10.1007/978-3-031-20105-9$$2doi
001453203 035__ $$aSP(OCoLC)1350415117
001453203 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dOCLCF
001453203 049__ $$aISEA
001453203 050_4 $$aQA76.9.A43
001453203 08204 $$a005.1$$223/eng/20221110
001453203 1001_ $$aCuevas, Erik.
001453203 24510 $$aAnalysis and comparison of metaheuristics /$$cErik Cuevas, Omar Avalos, Jorge Gálvez.
001453203 260__ $$aCham :$$bSpringer,$$c[2023]
001453203 300__ $$a1 online resource (230 p.).
001453203 4901_ $$aStudies in computational intelligence ;$$vv. 1063
001453203 500__ $$a6.3.1 Solitary Phase (A)
001453203 5050_ $$aIntro -- Preface -- Contents -- 1 Fundamentals of Metaheuristic Computation -- 1.1 Formulation of an Optimization Problem -- 1.2 Classical Optimization Methods -- 1.3 Metaheuristic Computation Schemes -- 1.4 Generic Structure of a Metaheuristic Method -- References -- 2 A Comparative Approach for Two-Dimensional Digital IIR Filter Design Applying Different Evolutionary Computational Techniques -- 2.1 Introduction -- 2.2 Evolutionary Computation Algorithms -- 2.2.1 Particle Swarm Optimization (PSO) -- 2.2.2 Artificial Bee Colony (ABC) -- 2.2.3 Differential Evolution (DE)
001453203 5058_ $$a2.2.4 Harmony Search (HS) -- 2.2.5 Gravitational Search Algorithm (GSA) -- 2.2.6 Flower Pollination Algorithm (FPA) -- 2.3 2D-IIR Filter Design Procedure -- 2.3.1 Comparative Parameter Setting -- 2.4 Experimental Results -- 2.4.1 Accuracy Comparison -- 2.4.2 Convergence Study -- 2.4.3 Computational Cost -- 2.4.4 Comparison with Different Bandwidth Sizes -- 2.4.5 Filter Performance Features -- 2.4.6 Statistical Non-parametrical Analysis -- 2.4.7 Filter Design Study in Images -- 2.5 Conclusions -- References -- 3 Comparison of Metaheuristics for Chaotic Systems Estimation -- 3.1 Introduction
001453203 5058_ $$a3.2 Evolutionary Computation Techniques (ECT) -- 3.2.1 Particle Swarm Optimization (PSO) -- 3.2.2 Artificial Bee Colony (ABC) -- 3.2.3 Cuckoo Search (CS) -- 3.2.4 Harmony Search (HS) -- 3.2.5 Differential Evolution (DE) -- 3.2.6 Gravitational Search Algorithm (GSA) -- 3.3 Parameter Estimation for Chaotic Systems (CS) -- 3.4 Experimental Results -- 3.4.1 Chaotic System Parameter Estimation -- 3.4.2 Statistical Analysis -- 3.5 Conclusions -- References -- 4 Comparison Study of Novel Evolutionary Algorithms for Elliptical Shapes in Images -- 4.1 Introduction -- 4.2 Problem Definition
001453203 5058_ $$a4.2.1 Multiple Ellipse Detection -- 4.3 Evolutionary Optimization Techniques -- 4.3.1 Grey Wolf Optimizer (GWO) Algorithm -- 4.3.2 Whale Optimizer Algorithm (WOA) -- 4.3.3 Crow Search Algorithm (CSA) -- 4.3.4 Gravitational Search Algorithm (GSA) -- 4.3.5 Cuckoo Search (CS) Method -- 4.4 Comparative Perspective of the Five Metaheuristic Methods -- 4.5 Experimental Simulation Results -- 4.5.1 Performance Metrics -- 4.5.2 Experimental Comparison Study -- 4.6 Conclusions -- References -- 5 IIR System Identification Using Several Optimization Techniques: A Review Analysis -- 5.1 Introduction
001453203 5058_ $$a5.2 Evolutionary Computation (EC) Algorithms -- 5.2.1 Particle Swarm Optimization (PSO) -- 5.2.2 The Artificial Bee Colony (ABC) -- 5.2.3 The Electromagnetism-Like (EM) Technique -- 5.2.4 Cuckoo Search (CS) Technique -- 5.2.5 Flower Pollination Algorithm (FPA) -- 5.3 Formulation of IIR Model Identification -- 5.4 Experimental Results -- 5.4.1 Results of IIR Model Identification -- 5.4.2 Statistical Study -- 5.5 Conclusions -- References -- 6 Fractional-Order Estimation Using via Locust Search Algorithm -- 6.1 Introduction -- 6.2 Fractional Calculus -- 6.3 Locust Search (LS) Algorithm
001453203 506__ $$aAccess limited to authorized users.
001453203 520__ $$aThis book presents a comparative perspective of current metaheuristic developments, which have proved to be effective in their application to several complex problems. The study of biological and social entities such as animals, humans, or insects that manifest a cooperative behavior has produced several computational models in metaheuristic methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. Under such conditions, it is not clear to identify which are the advantages or disadvantages of each metaheuristic technique. The book is compiled from a teaching perspective. For this reason, the book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. It is appropriate for courses such as Artificial Intelligence, Electrical Engineering, Evolutionary Computation. The book is also useful for researchers from the evolutionary and engineering communities. Likewise, engineer practitioners, who are not familiar with metaheuristic computation concepts, will appreciate that the techniques discussed are beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise in engineering areas.
001453203 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 10, 2022).
001453203 650_0 $$aMetaheuristics.
001453203 655_0 $$aElectronic books.
001453203 7001_ $$aAvalos, Omar.
001453203 7001_ $$aGálvez, Jorge.
001453203 77608 $$iPrint version:$$aCuevas, Erik$$tAnalysis and Comparison of Metaheuristics$$dCham : Springer International Publishing AG,c2022$$z9783031201042
001453203 830_0 $$aStudies in computational intelligence ;$$vv. 1063.
001453203 852__ $$bebk
001453203 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-20105-9$$zOnline Access$$91397441.1
001453203 909CO $$ooai:library.usi.edu:1453203$$pGLOBAL_SET
001453203 980__ $$aBIB
001453203 980__ $$aEBOOK
001453203 982__ $$aEbook
001453203 983__ $$aOnline
001453203 994__ $$a92$$bISE