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Advancements in applied metaheuristic computing[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.3
書名/作者:	Advancements in applied metaheuristic computing/ Nilanjan Dey, editor.
其他作者:	Dey, Nilanjan,
出版者:	Hershey, Pennsylvania : : IGI Global,, [2018]
面頁冊數:	1 online resource (xxi, 335 p.)
標題:	Systems engineering - Data processing.
標題:	Heuristic algorithms.
標題:	Mathematical optimization.
標題:	Artificial intelligence.
ISBN:	9781522541523 (ebook)
ISBN:	9781522541516 (hardcover)
書目註:	Includes bibliographical references and index.
內容註:	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.
摘要、提要註:	"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"--
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4151-6

Advancements in applied metaheuristic computing[electronic resource] /
Advancements in applied metaheuristic computing[electronic resource] /Nilanjan Dey, editor. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xxi, 335 p.)

Includes bibliographical references and index.

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.

Restricted to subscribers or individual electronic text purchasers.

"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"--

ISBN: 9781522541523 (ebook)Subjects--Topical Terms:

481481
Systems engineering
--Data processing.

LC Class. No.: TA168 / .A286 2018e

Dewey Class. No.: 006.3

Advancements in applied metaheuristic computing[electronic resource] /
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504 $a Includes bibliographical references and index.
505 0 $a 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.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "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"-- $c Provided by publisher.
650 0 $a Systems engineering $x Data processing. $3 481481
650 0 $a Heuristic algorithms. $3 481063
650 0 $a Mathematical optimization. $3 176332
650 0 $a Artificial intelligence. $3 172060
700 1 $a Dey, Nilanjan, $d 1984- $e editor. $3 690504
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4151-6