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Advancements in applied metaheuristic computing

Author: Nilanjan Dey; IGI Global,
Publisher: Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global, [2018]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"This book considers the foremost optimization algorithms in several applications. It deals primarily with methods and approaches that include meta-heuristics for further systems improvements. It grants substantial frameworks and the most contemporary empirical research outcomes in employing optimization algorithms"--
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Additional Physical Format: Print version:
(DLC) 2017028945
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nilanjan Dey; IGI Global,
ISBN: 9781522541523 1522541527
OCLC Number: 1011023965
Description: 1 online resource (17 PDFs (xxi, 335 pages))
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: predictive modeling of tool wear in machining using genetic programming ; Chapter 6. Intelligent computing in medical imaging: a study ; 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: a review --
Section 2. Genetic algorithm applications. Chapter 9. Optimized crossover jumpX in genetic algorithm for general routing problems: a crossover survey and enhancement ; 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: II-neural network approach.
Responsibility: Nilanjan Dey, editor.

Abstract:

Presents the latest empirical research on methods and approaches that include metaheuristics for further system improvements, and offers outcomes of employing optimization algorithms. Featuring  Read more...

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