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  • 1
    Online Resource
    Online Resource
    Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic (2018)
    Cham : Springer International Publishing
    Details
    UID:
    b3kat_BV044888429
    Format: 1 Online-Ressource (VII, 105 p. 25 illus)
    ISBN: 9783319708515
    Series Statement: SpringerBriefs in Applied Sciences and Technology
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-319-70850-8
    Language: English
    DOI: 10.1007/978-3-319-70851-5
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Melin, Patricia 1962-
    Author information: Castillo, Oscar 1959-
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  • 2
    Online Resource
    Online Resource
    New backpropagation algorithm with type-2 fuzzy weights for neural networks (2016)
    [Cham] : Springer
    Details
    UID:
    b3kat_BV043650416
    Format: 1 Online-Ressource (ix, 102 Seiten)
    ISBN: 9783319340876
    Series Statement: SpringerBriefs in applied sciences and technology : Computational intelligence
    Additional Edition: Erscheint auch als Druckausgabe ISBN 978-3-319-34086-9
    Language: English
    DOI: 10.1007/978-3-319-34087-6
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Melin, Patricia 1962-
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  • 3
    Online Resource
    Online Resource
    A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature / (2019)
    Cham :Springer International Publishing :
    Details
    UID:
    almafu_9959767540502883
    Format: 1 online resource (VIII, 57 p.)
    Edition: 1st ed. 2019.
    ISBN: 3-030-05551-5
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book presents a new meta-heuristic algorithm, inspired by the self-defense mechanisms of plants in nature. Numerous published works have demonstrated the various self-defense mechanisms (survival strategies) plants use to protect themselves against predatory organisms, such as herbivorous insects. The proposed algorithm is based on the predator–prey mathematical model originally proposed by Lotka and Volterra, consisting of two nonlinear first-order differential equations, which allow the growth of two interacting populations (prey and predator) to be modeled. The proposed meta-heuristic is able to produce excellent results in several sets of benchmark optimization problems. Further, fuzzy logic is used for dynamic parameter adaptation in the algorithm.
    Note: Introduction -- Theory and Background -- Self-defense of the Plants -- Predator-prey mode -- Proposed Method -- Case studies -- Conclusions.
    Additional Edition: ISBN 3-030-05550-7
    Language: English
    DOI: 10.1007/978-3-030-05551-6
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  • 4
    Online Resource
    Online Resource
    Hybrid Competitive Learning Method Using the Fireworks Algorithm and Artificial Neural Networks / (2023)
    Cham, Switzerland :Springer,
    Details
    UID:
    almafu_9961350504102883
    Format: 1 online resource (112 pages)
    Edition: First edition.
    ISBN: 3-031-47712-X
    Series Statement: SpringerBriefs in Applied Sciences and Technology Series
    Note: Intro -- Preface -- Contents -- 1 Introduction to the Hybrid Method Between Fireworks Algorithm and Competitive Neural Network -- References -- 2 Basic Concepts of Neural Networks and Fuzzy Logic -- 2.1 Artificial Neural Networks -- 2.2 Competitive Artificial Neural Network (CNN) -- 2.3 Optimization Algorithms -- 2.4 Fireworks Algorithm (FWA) -- 2.5 Clustering Algorithms -- 2.6 Clustering Index Validation -- 2.7 Fuzzy Logic -- 2.8 Interval Type-2 Fuzzy Logic -- References -- 3 Hybrid Method Between Fireworks Algorithm and Competitive Neural Network -- 3.1 Datasets -- 3.1.1 Iris Dataset -- 3.1.2 Wine Dataset -- 3.1.3 Breast Cancer Wisconsin Diagnostic (WDBC) -- 3.2 Optimization Methods -- 3.2.1 FWAC -- 3.2.2 FFWAC -- 3.2.3 F2FWAC -- 3.3 Clustering Method -- 3.3.1 Competitive Neural Network -- 3.4 Classification Methods (Fuzzy Classifiers) -- 3.4.1 T1FIS-Mamdani and Sugeno Type -- 3.4.2 IT2FIS-Mamdani and Sugeno Type -- References -- 4 Simulation Studies of the Hybrid of Fuzzy Fireworks and Competitive Neural Networks -- 4.1 Optimization Methods -- 4.1.1 Fireworks Algorithm for Clustering (FWAC) -- 4.1.2 Type 1 Fuzzy Logic Fireworks Algorithm for Clustering (FFWAC) -- 4.1.3 Interval Type 2 Fuzzy Logic Fireworks Algorithm for Clustering (F2FFWA) -- 4.2 Classification Methods -- 4.2.1 Type 1 FIS Classifier (T1FIS) -- 4.2.2 Interval Type 2 Fuzzy Logic Classifiers (IT2FIS) -- 4.3 Statistical Comparison Results of the Optimization of the Fuzzy Classification Models -- 5 Conclusions to the Hybrid Method Between Fireworks Algorithm and Competitive Neural Network -- 5.1 Future Works -- Index.
    Additional Edition: Print version: Valdez, Fevrier Hybrid Competitive Learning Method Using the Fireworks Algorithm and Artificial Neural Networks Cham : Springer,c2024
    Language: English
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  • 5
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    Online Resource
    Mycorrhiza Optimization Algorithm. (2023)
    Cham :Springer International Publishing AG,
    Details
    UID:
    almafu_9961308462102883
    Format: 1 online resource (84 pages)
    Edition: 1st ed.
    ISBN: 3-031-47369-8
    Series Statement: SpringerBriefs in Applied Sciences and Technology Series
    Note: Intro -- Preface -- Contents -- 1 Introduction to the Mycorrhiza Optimization Algorithm -- References -- 2 Theory for the Mycorrhiza Optimization Algorithm -- 2.1 Optimization -- 2.2 Mycorrhiza Networks Structure and Function -- 2.3 Characteristics of Mycorrhized Plants -- 2.4 The Mother Trees -- 2.5 Wood Wide Web -- References -- 3 Background for the Mycorrhiza Optimization Algorithm -- References -- 4 Problem Definition for Mycorrhiza Algorithm -- References -- 5 CMOA-Continuous Mycorrhiza Optimization Algorithm -- 5.1 CMOA Flowchart -- 5.2 CMOA Pseudocode -- 5.3 Continuous Lotka-Volterra System Equations -- 5.4 CMOA Algorithm Parameters -- 5.5 Benchmark Functions -- 5.6 Case Studies -- 5.6.1 CMOA Predator-Prey Model -- 5.6.2 CMOA Cooperative Model -- 5.6.3 CMOA Competitive Model -- 5.6.4 CMOA Ecosystem Model -- 5.6.5 Results -- 5.6.6 Hypothesis Test -- 5.6.7 Comparative CMOA with Others Methods -- 5.6.8 Results -- 5.6.9 Behavior -- 5.6.10 Convergence -- References -- 6 DMOA-Discrete Mycorrhiza Optimization Algorithm -- 6.1 DMOA Flowchart -- 6.2 DMOA Pseudocode -- 6.3 Discrete Lotka-Volterra System Equations -- 6.4 DMOA Algorithm Parameters -- 6.5 Case Studies -- 6.5.1 DMOA Ecosystem Model -- 6.5.2 Results -- 6.5.3 Comparative DMOA with Other Methods -- 6.5.4 Results -- 6.5.5 Hypothesis Test -- 6.5.6 Comparative CMOA with DMOA -- 6.5.7 Behavior Comparative with Other Methods -- 6.5.8 Behavior Comparative CMOA Versus DMOA -- 6.5.9 Convergence Comparative CMOA Versus DMOA -- References -- 7 Conclusions of the Mycorrhiza Optimization Algorithm -- Index.
    Additional Edition: Print version: Valdez, Fevrier Mycorrhiza Optimization Algorithm Cham : Springer International Publishing AG,c2023 ISBN 9783031473685
    Language: English
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  • 6
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    Online Resource
    General Type-2 Fuzzy Logic in Dynamic Parameter Adaptation for the Harmony Search Algorithm / (2020)
    Cham :Springer International Publishing :
    Details
    UID:
    almafu_9959767594702883
    Format: 1 online resource (86 pages).
    Edition: 1st ed. 2020.
    ISBN: 3-030-43950-X
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book focuses on the fields of fuzzy logic and metaheuristic algorithms, particularly the harmony search algorithm and fuzzy control. There are currently several types of metaheuristics used to solve a range of real-world of problems, and these metaheuristics contain parameters that are usually fixed throughout the iterations. However, a number of techniques are also available that dynamically adjust the parameters of an algorithm, such as probabilistic fuzzy logic. This book proposes a method of addressing the problem of parameter adaptation in the original harmony search algorithm using type-1, interval type-2 and generalized type-2 fuzzy logic. The authors applied this methodology to the resolution of problems of classical benchmark mathematical functions, CEC 2015, CEC2017 functions and to the optimization of various fuzzy logic control cases, and tested the method using six benchmark control problems – four of the Mamdani type: the problem of filling a water tank, the problem of controlling the temperature of a shower, the problem of controlling the trajectory of an autonomous mobile robot and the problem of controlling the speed of an engine; and two of the Sugeno type: the problem of controlling the balance of a bar and ball, and the problem of controlling control the balance of an inverted pendulum. When the interval type-2 fuzzy logic system is used to model the behavior of the systems, the results show better stabilization because the uncertainty analysis is better. As such, the authors conclude that the proposed method, based on fuzzy systems, fuzzy controllers and the harmony search optimization algorithm, improves the behavior of complex control plants.
    Note: Introduction to Fuzzy Harmony Search -- Theory of the Original Harmony Search Method -- Proposed Fuzzy Harmony Search Method -- Study Cases -- Conclusion.
    Additional Edition: ISBN 3-030-43949-6
    Language: English
    DOI: 10.1007/978-3-030-43950-7
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  • 7
    Online Resource
    Online Resource
    General Type-2 Fuzzy Logic in Dynamic Parameter Adaptation for the Harmony Search Algorithm (2020)
    Cham :Springer International Publishing :
    Details
    UID:
    almahu_9948336365602882
    Format: VII, 83 p. 47 illus., 32 illus. in color. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9783030439507
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book focuses on the fields of fuzzy logic and metaheuristic algorithms, particularly the harmony search algorithm and fuzzy control. There are currently several types of metaheuristics used to solve a range of real-world of problems, and these metaheuristics contain parameters that are usually fixed throughout the iterations. However, a number of techniques are also available that dynamically adjust the parameters of an algorithm, such as probabilistic fuzzy logic. This book proposes a method of addressing the problem of parameter adaptation in the original harmony search algorithm using type-1, interval type-2 and generalized type-2 fuzzy logic. The authors applied this methodology to the resolution of problems of classical benchmark mathematical functions, CEC 2015, CEC2017 functions and to the optimization of various fuzzy logic control cases, and tested the method using six benchmark control problems - four of the Mamdani type: the problem of filling a water tank, the problem of controlling the temperature of a shower, the problem of controlling the trajectory of an autonomous mobile robot and the problem of controlling the speed of an engine; and two of the Sugeno type: the problem of controlling the balance of a bar and ball, and the problem of controlling control the balance of an inverted pendulum. When the interval type-2 fuzzy logic system is used to model the behavior of the systems, the results show better stabilization because the uncertainty analysis is better. As such, the authors conclude that the proposed method, based on fuzzy systems, fuzzy controllers and the harmony search optimization algorithm, improves the behavior of complex control plants.
    Note: Introduction to Fuzzy Harmony Search -- Theory of the Original Harmony Search Method -- Proposed Fuzzy Harmony Search Method -- Study Cases -- Conclusion.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783030439491
    Additional Edition: Printed edition: ISBN 9783030439514
    Language: English
    DOI: 10.1007/978-3-030-43950-7
    URL: https://doi.org/10.1007/978-3-030-43950-7
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  • 8
    Online Resource
    Online Resource
    A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature (2019)
    Cham :Springer International Publishing :
    Details
    UID:
    almahu_9948029446902882
    Format: VIII, 57 p. , online resource.
    ISBN: 9783030055516
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book presents a new meta-heuristic algorithm, inspired by the self-defense mechanisms of plants in nature. Numerous published works have demonstrated the various self-defense mechanisms (survival strategies) plants use to protect themselves against predatory organisms, such as herbivorous insects. The proposed algorithm is based on the predator–prey mathematical model originally proposed by Lotka and Volterra, consisting of two nonlinear first-order differential equations, which allow the growth of two interacting populations (prey and predator) to be modeled. The proposed meta-heuristic is able to produce excellent results in several sets of benchmark optimization problems. Further, fuzzy logic is used for dynamic parameter adaptation in the algorithm.
    Note: Introduction -- Theory and Background -- Self-defense of the Plants -- Predator-prey mode -- Proposed Method -- Case studies -- Conclusions.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783030055509
    Additional Edition: Printed edition: ISBN 9783030055523
    Language: English
    DOI: 10.1007/978-3-030-05551-6
    URL: https://doi.org/10.1007/978-3-030-05551-6
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  • 9
    Online Resource
    Online Resource
    Mycorrhiza Optimization Algorithm (2023)
    Cham :Springer Nature Switzerland :
    Details
    UID:
    almahu_9949578751902882
    Format: VIII, 78 p. 32 illus., 26 illus. in color. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9783031473692
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book provides two new optimization algorithms to address real optimization problems. Optimization is a fundamental concept in engineering and science, and its applications are needed in many fields. From designing products and systems to developing algorithms and models, optimization plays a critical role in achieving efficient and effective solutions to complex problems. Optimization algorithms inspired by nature have proven effective in solving a wide range of problems, including those in engineering, finance, and machine learning. These algorithms are often used when traditional optimization techniques are impractical due to the size or complexity of the problem. In this book, we are presenting two new optimization algorithms inspired by plant roots and the Mycorrhiza Network. The first algorithm is called the Continuous Mycorrhiza Optimization Algorithm (CMOA), which was proposed based on the model of the Continuous Lotka-Volterra System Equations. The second algorithm is called the Discrete Mycorrhiza Optimization Algorithm (DMOA), which design based on the model of Discrete Lotka-Volterra System Equations. By mastering the proposed algorithms, the readers able to develop innovative solutions that improve efficiency, reduce costs, and improve performance in the corresponding field of work.
    Note: Introduction to the Mycorrhiza Optimization Algorithm -- Theory for the Mycorrhiza Optimization Algorithm -- Background for the Mycorrhiza Algorithm -- Problem Definition -- CMOA - Continuous Mycorrhiza Optimization Algorithm -- DMOA - Discrete Mycorrhiza Optimization Algorithm -- Conclusions of the Mycorrhiza Optimization Algorithm.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031473685
    Additional Edition: Printed edition: ISBN 9783031473708
    Language: English
    DOI: 10.1007/978-3-031-47369-2
    URL: https://doi.org/10.1007/978-3-031-47369-2
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  • 10
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    Online Resource
    Hybrid Competitive Learning Method Using the Fireworks Algorithm and Artificial Neural Networks (2023)
    Cham :Springer Nature Switzerland :
    Details
    UID:
    almahu_9949599154802882
    Format: VIII, 103 p. 32 illus., 19 illus. in color. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9783031477126
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book focuses on the fields of neural networks, swarm optimization algorithms, clustering and fuzzy logic. This book describes a hybrid method with three different techniques of intelligence computation: neural networks, optimization algorithms and fuzzy logic. Within the neural network techniques, competitive neural networks (CNNs) are used, for the optimization algorithms technique, we used the fireworks algorithm (FWA), and in the area of fuzzy logic, the Type-1 Fuzzy Inference Systems (T1FIS) and the Interval Type-2 Fuzzy Inference Systems (IT2FIS) were used, with their variants of Mamdani and Sugeno type, respectively. FWA was adapted for data clustering with the goal to help of competitive neural network to find the optimal number of neurons. It is important to mention that two variants were applied to the FWA: dynamically adjust of parameters with Type-1 Fuzzy Logic (FFWA) as the first one and Interval Type-2 (F2FWA) as the second one. Subsequently, based on the outputs of the CNN and with the goal of classification data, we designed Type-1 and Interval Type-2 Fuzzy Inference Systems of Mamdani and Sugeno type. This book is intended to be a reference for scientists and engineers interested in applying a different metaheuristic or an artificial neural network in order to solve optimization and applied fuzzy logic techniques for solving problems in clustering and classification data. This book is also used as a reference for graduate courses like the following: soft computing, swarm optimization algorithms, clustering data, fuzzy classify and similar ones. We consider that this book can also be used to get novel ideas for new lines of research, new techniques of optimization or to continue the lines of the research proposed by the authors of the book.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031477119
    Additional Edition: Printed edition: ISBN 9783031477133
    Language: English
    DOI: 10.1007/978-3-031-47712-6
    URL: https://doi.org/10.1007/978-3-031-47712-6
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