Handbook of Nature Inspired Optimization Algorithms The State of the Art

This book PDF is perfect for those who love Technology & Engineering genre, written by Ali Mohamed and published by Springer Nature which was released on 31 August 2022 with total hardcover pages 282. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Handbook of Nature Inspired Optimization Algorithms The State of the Art books below.

Handbook of Nature Inspired Optimization Algorithms The State of the Art
Handbook of Nature Inspired Optimization Algorithms The State of the Art
Author : Ali Mohamed
File Size : 48,6 Mb
Publisher : Springer Nature
Language : English
Release Date : 31 August 2022
ISBN : 9783031075124
Pages : 282 pages
Get Book

Handbook of Nature Inspired Optimization Algorithms The State of the Art by Ali Mohamed Book PDF Summary

The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving. The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.

Handbook of Nature Inspired Optimization Algorithms The State of the Art

Handbook of Nature Inspired Optimization Algorithms The State of the Art by Ali Mohamed,Diego Oliva,Ponnuthurai Nagaratnam Suganthan

The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties

Get Book
Handbook of Nature Inspired Optimization Algorithms The State of the Art

Handbook of Nature Inspired Optimization Algorithms The State of the Art by Ali Wagdy Mohamed,Diego Oliva,Ponnuthurai Nagaratnam Suganthan

This book presents recent contributions and significant development, advanced issues, and challenges. In real-world problems and applications, most of the optimization problems involve different types of constraints. These problems are called constrained optimization problems (COPs). The optimization of the constrained optimization problems is considered a challenging task since the optimum

Get Book
Nature Inspired Optimization Algorithms

Nature Inspired Optimization Algorithms by Vasuki A

Nature-Inspired Optimization Algorithms, a comprehensive work on the most popular optimization algorithms based on nature, starts with an overview of optimization going from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna that inspired the development of optimization techniques, providing us with

Get Book
Handbook of Research on Soft Computing and Nature Inspired Algorithms

Handbook of Research on Soft Computing and Nature Inspired Algorithms by Shandilya, Shishir K.,Shandilya, Smita,Deep, Kusum,Nagar, Atulya K.

Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research on Soft Computing and Nature-Inspired Algorithms is an essential source for the latest scholarly

Get Book
Nature inspired Metaheuristic Algorithms

Nature inspired Metaheuristic Algorithms by Xin-She Yang

Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization,

Get Book
Nature inspired Optimization Algorithms and Soft Computing

Nature inspired Optimization Algorithms and Soft Computing by Rajeev Arya,Sangeeta Singh,Maheshwari P. Singh,Brijesh R. Iyer,Venkat N. Gudivada

This edited book reviews the intertwining disciplines of nature-inspired optimization algorithms and bio-inspired soft-computing for real world applications, with the interaction between metaheuristics with complex systems. The authors present methods and techniques in IoT, image processing, smart manufacturing and healthcare.

Get Book
Metaheuristic Optimization Nature Inspired Algorithms Swarm and Computational Intelligence Theory and Applications

Metaheuristic Optimization Nature Inspired Algorithms Swarm and Computational Intelligence Theory and Applications by Modestus O. Okwu,Lagouge K. Tartibu

This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of

Get Book
Nature Inspired Optimization Algorithms

Nature Inspired Optimization Algorithms by Xin-She Yang

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing,

Get Book