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Constraint handling in metaheuristic...

Kulkarni, Anand J.

Constraint handling in metaheuristics and applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Constraint handling in metaheuristics and applicationsedited by Anand J. Kulkarni ... [et al.].
other author:	Kulkarni, Anand J.
Published:	Singapore :Springer Singapore :2021.
Description:	xxix, 315 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Metaheuristics.
Online resource:	https://doi.org/10.1007/978-981-33-6710-4
ISBN:	9789813367104$q(electronic bk.)

Constraint handling in metaheuristics and applications
Constraint handling in metaheuristics and applications[electronic resource] /edited by Anand J. Kulkarni ... [et al.]. - Singapore :Springer Singapore :2021. - xxix, 315 p. :ill., digital ;24 cm.

1. The Find-Fix-Finish-Exploit-Analyze (F3EA) meta-heuristic algorithm with an extended constraint handling technique for constrained optimization and engineering design -- An improved Cohort Intelligence with Panoptic Learning Behavior for solving constrained problems -- Nature-Inspired Metaheuristic Algorithms for Constraint Handling: Challenges, Issues and Research Perspective.

This book aims to discuss the core and underlying principles and analysis of the different constraint handling approaches. The main emphasis of the book is on providing an enriched literature on mathematical modelling of the test as well as real-world problems with constraints, and further development of generalized constraint handling techniques. These techniques may be incorporated in suitable metaheuristics providing a solid optimized solution to the problems and applications being addressed. The book comprises original contributions with an aim to develop and discuss generalized constraint handling approaches/techniques for the metaheuristics and/or the applications being addressed. A variety of novel as well as modified and hybridized techniques have been discussed in the book. The conceptual as well as the mathematical level in all the chapters is well within the grasp of the scientists as well as the undergraduate and graduate students from the engineering and computer science streams. The reader is encouraged to have basic knowledge of probability and mathematical analysis and optimization. The book also provides critical review of the contemporary constraint handling approaches. The contributions of the book may further help to explore new avenues leading towards multidisciplinary research discussions. This book is a complete reference for engineers, scientists, and students studying/working in the optimization, artificial intelligence (AI), or computational intelligence arena.

ISBN: 9789813367104$q(electronic bk.)

Standard No.: 10.1007/978-981-33-6710-4doiSubjects--Topical Terms:

757062
Metaheuristics.

LC Class. No.: QA76.9.A43 / C65 2021

Dewey Class. No.: 519.6

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505 0 $a 1. The Find-Fix-Finish-Exploit-Analyze (F3EA) meta-heuristic algorithm with an extended constraint handling technique for constrained optimization and engineering design -- An improved Cohort Intelligence with Panoptic Learning Behavior for solving constrained problems -- Nature-Inspired Metaheuristic Algorithms for Constraint Handling: Challenges, Issues and Research Perspective.
520 $a This book aims to discuss the core and underlying principles and analysis of the different constraint handling approaches. The main emphasis of the book is on providing an enriched literature on mathematical modelling of the test as well as real-world problems with constraints, and further development of generalized constraint handling techniques. These techniques may be incorporated in suitable metaheuristics providing a solid optimized solution to the problems and applications being addressed. The book comprises original contributions with an aim to develop and discuss generalized constraint handling approaches/techniques for the metaheuristics and/or the applications being addressed. A variety of novel as well as modified and hybridized techniques have been discussed in the book. The conceptual as well as the mathematical level in all the chapters is well within the grasp of the scientists as well as the undergraduate and graduate students from the engineering and computer science streams. The reader is encouraged to have basic knowledge of probability and mathematical analysis and optimization. The book also provides critical review of the contemporary constraint handling approaches. The contributions of the book may further help to explore new avenues leading towards multidisciplinary research discussions. This book is a complete reference for engineers, scientists, and students studying/working in the optimization, artificial intelligence (AI), or computational intelligence arena.
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