Advancements in Applied Metaheuristic Computing
by: Nilanjan Dey
Pages: 335 pages
Edition 版本: 1
Language 语言: English
Publisher finelybook 出版社: IGI Global
Released: 2017-11-30
ISBN-10: 1522541519
ISBN-13: 9781522541516
Book Description
Metaheuristic algorithms are present in various applications for different domains. Recently,researchers have conducted studies on the effectiveness of these algorithms in providing optimal solutions to complicated problems.
Advancements in Applied Metaheuristic Computing is a crucial reference source for the latest empirical research on methods and approaches that include metaheuristics for further system improvements,and it offers outcomes of employing optimization algorithms. Featuring coverage on a broad range of topics such as manufacturing,genetic programming,and medical imaging,this publication is ideal for researchers,academicians,advanced-level students,and technology developers seeking current research on the use of optimization algorithms in several applications.
Contents
Section 1: Meta-Heuristic Optimization-Algorithms-Based Advanced Applications
Chapter 1: Multi-Objective Optimal Power Flow Using Metaheuristic Optimization Algorithms With Unified Power Flow Controller to Enhance the Power System Performance
Chapter 2: Analyzing and Predicting the QoS of Traffic in WiMAX Network Using Gene Expression Programming
Chapter 3: Chaotic Differential-Evolution-Based Fuzzy Contrast Stretching Method
Chapter 4: Protein Motif Comparator Using Bio-Inspired Two-Way K-Means
Chapter 5: Metaheuristics in Manufacturing
Chapter 6: Intelligent Computing in Medical Imaging
Chapter 7: Effect of SMES Unit in AGC of an Interconnected Multi-Area Thermal Power System With ACO-Tuned PID Controller
Chapter 8: Meta-Heuristic Algorithms in Medical Image Segmentation
Section 2: Genetic Algorithm Applications
Chapter 9: Optimized Crossover JumpX in Genetic Algorithm for General Routing Problems
Chapter 10: On Developing and Performance Evaluation of Adaptive Second Order Neural Network With GA-Based Training (ASONN-GA) for Financial Time Series Prediction
Chapter 11: Hybrid Non-Dominated Sorting Genetic Algorithm
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