國立高雄大學圖資館 |

Language: English

Back

Metaheuristic algorithmsnew methods, evaluation, and performance analysis /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Metaheuristic algorithmsby Erik Cuevas ... [et al.].
Reminder of title:	new methods, evaluation, and performance analysis /
other author:	Cuevas, Erik.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xv, 297 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Metaheuristics.
Online resource:	https://doi.org/10.1007/978-3-031-63053-8
ISBN:	9783031630538$q(electronic bk.)

Metaheuristic algorithmsnew methods, evaluation, and performance analysis /
Metaheuristic algorithmsnew methods, evaluation, and performance analysis /[electronic resource] :by Erik Cuevas ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xv, 297 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 11631860-9503 ;. - Studies in computational intelligence ;v. 216..

1. Introduction to Metaheuristic methods. -- 2. A novel method for initializing populations using the Metropolis-Hastings (MH) technique. -- 3. A measure of diversity for metaheuristic algorithms employing population-based approaches. -- 4. Population Control in Metaheuristic Algorithms: Can Fewer Be Better?. -- 5. Exploration Paths Derived from Trajectories Extracted from Second-Order System Responses. -- 6. Utilizing the Moth Swarm Algorithm to Improve Image Contrast. -- 7. Enhancing Anisotropic Diffusion Filtering via Multi-Objective Optimization. -- 8. Fractional Fuzzy Controller Calibration Using metaheuristic Techniques. -- 9. Striving for Optimal Equilibrium in Metaheuristic Algorithms: Is It Attainable?.

This book encompasses three distinct yet interconnected objectives. Firstly, it aims to present and elucidate novel metaheuristic algorithms that feature innovative search mechanisms, setting them apart from conventional metaheuristic methods. Secondly, this book endeavors to systematically assess the performance of well-established algorithms across a spectrum of intricate and real-world problems. Finally, this book serves as a vital resource for the analysis and evaluation of metaheuristic algorithms. It provides a foundational framework for assessing their performance, particularly in terms of the balance between exploration and exploitation, as well as their capacity to obtain optimal solutions. Collectively, these objectives contribute to advancing our understanding of metaheuristic methods and their applicability in addressing diverse and demanding optimization tasks. The materials were 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. Additionally, engineering practitioners who are not familiar with metaheuristic computation concepts will appreciate that the techniques discussed are beyond simple theoretical tools because they have been adapted to solve significant problems that commonly arise in engineering areas.

ISBN: 9783031630538$q(electronic bk.)

Standard No.: 10.1007/978-3-031-63053-8doiSubjects--Topical Terms:

757062
Metaheuristics.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 518.1

Metaheuristic algorithmsnew methods, evaluation, and performance analysis /
LDR:03216nmm a2200337 a 4500 001 658414
003 DE-He213
005 20240626125403.0
006 m d
007 cr nn 008maaau
008 240923s2024 sz s 0 eng d
020 $a 9783031630538$q(electronic bk.)
020 $a 9783031630521$q(paper)
024 7 $a 10.1007/978-3-031-63053-8 $2 doi
035 $a 978-3-031-63053-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A43
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 518.1 $2 23
090 $a QA76.9.A43 $b M587 2024
245 0 0 $a Metaheuristic algorithms $h [electronic resource] : $b new methods, evaluation, and performance analysis / $c by Erik Cuevas ... [et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xv, 297 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1163
505 0 $a 1. Introduction to Metaheuristic methods. -- 2. A novel method for initializing populations using the Metropolis-Hastings (MH) technique. -- 3. A measure of diversity for metaheuristic algorithms employing population-based approaches. -- 4. Population Control in Metaheuristic Algorithms: Can Fewer Be Better?. -- 5. Exploration Paths Derived from Trajectories Extracted from Second-Order System Responses. -- 6. Utilizing the Moth Swarm Algorithm to Improve Image Contrast. -- 7. Enhancing Anisotropic Diffusion Filtering via Multi-Objective Optimization. -- 8. Fractional Fuzzy Controller Calibration Using metaheuristic Techniques. -- 9. Striving for Optimal Equilibrium in Metaheuristic Algorithms: Is It Attainable?.
520 $a This book encompasses three distinct yet interconnected objectives. Firstly, it aims to present and elucidate novel metaheuristic algorithms that feature innovative search mechanisms, setting them apart from conventional metaheuristic methods. Secondly, this book endeavors to systematically assess the performance of well-established algorithms across a spectrum of intricate and real-world problems. Finally, this book serves as a vital resource for the analysis and evaluation of metaheuristic algorithms. It provides a foundational framework for assessing their performance, particularly in terms of the balance between exploration and exploitation, as well as their capacity to obtain optimal solutions. Collectively, these objectives contribute to advancing our understanding of metaheuristic methods and their applicability in addressing diverse and demanding optimization tasks. The materials were 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. Additionally, engineering practitioners who are not familiar with metaheuristic computation concepts will appreciate that the techniques discussed are beyond simple theoretical tools because they have been adapted to solve significant problems that commonly arise in engineering areas.
650 0 $a Metaheuristics. $3 757062
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Cuevas, Erik. $3 737835
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
856 4 0 $u https://doi.org/10.1007/978-3-031-63053-8
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)