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Könyv Metaheuristics and Optimization in Computer and Electrical Engineering Navid Razmjooy

Metaheuristics and Optimization in Computer and Electrical Engineering

Volume 2: Hybrid and Improved Algorithms

Szerző: Navid Razmjooy, Noradin Ghadimi, Venkatesan Rajinikanth
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: Springer, Berlin
Elérhetőség: Beszállítói készleten alacsony példányszámban
Küldés 11-15 napon belül
65 014 Ft
This book discusses different methods of modifying the original metaheuristics and their application...

Információk a könyvről

Szerző
Navid Razmjooy, Noradin Ghadimi, Venkatesan Rajinikanth
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2023
oldal
290
EAN
9783031426841
Enbook ID
43829823
Kiadó
Springer, Berlin
Súly
870
Méretek
155 x 235

Teljes leírás

This book discusses different methods of modifying the original metaheuristics and their application in computer and electrical engineering. As the race to develop advanced technology accelerates, a new era of "metaheuristics" has emerged. Through researched-based techniques and collaborative problem-solving, this book helps engineers to find efficient solutions to their engineering challenges. With the help of an expert guide and the collective knowledge of the engineering community, this comprehensive guide shows readers how to use machine learning and other AI techniques to reinvent smart engineering. From understanding the fundamentals to mastering the latest metaheuristics models, this guide provides with the skills and knowledge that need to stay ahead in the technology race. In the previous volume, authors focused on the application of original metaheuristics on electrical and computer sciences. This volume learns how AI and modified metaheuristics can be used to optimize algorithms and create more efficient electrical engineering designs. It gets insights on how data can be effectively processed and discover new techniques for creating sophisticated automation systems. It maximizes the potential of readers' computer and electrical engineering projects with powerful metaheuristics and optimization techniques.

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