книга Brain Storm Optimization Algorithms: Concepts, Principles and Applications

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Brain Storm Optimization Algorithms: Concepts, Principles and Applications

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Название: Brain Storm Optimization Algorithms: Concepts, Principles and Applications
Автор: Shi Cheng, Yuhui Shi
Страниц: 305
Формат: PDF
Размер: 10.1 MB
Качество: Отличное
Язык: Английский
Год издания: 2019


Brain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process. Since the introduction of BSO algorithms in 2011, many studies on them have been conducted. They not only offer an optimization method, but could also be viewed as a framework of optimization techniques. The process employed in the algorithms could be simplified as a framework with two basic operations: the converging operation and the diverging operation. A “good enough” optimum could be obtained through recursive solution divergence and convergence. The resulting optimization algorithm would naturally have the capability of both convergence and divergence.



The book is structured into three parts. In the first part, i.e., Chapter “Brain Storm Optimization Algorithms: More Questions than Answers”, a comprehensive introduction to the basic concepts, developments, and future research of BSO algorithms are described. Many works have been conducted on the BSO algorithms; however, there are still massive questions on this algorithm need to be studied.



The second part, i.e., the methodology part, containing Chapters “Brain Storm Optimization for Test Task Scheduling Problem”–“Brain Storm Algorithm Combined with Covariance Matrix Adaptation Evolution Strategy for Optimization”, introduces several variants of BSO algorithms. Chapter “Brain Storm Optimization for Test Task Scheduling Problem” introduces a modified BSO algorithm for solving the single-objective and the multi-objective test task scheduling problem (TTSP), respectively.



Finally, the third part, i.e., the applications part, contains Chapters “A Feature Extraction Method Based on BSO Algorithm for Flight Data”–“Enhancement of Voltage Stability Using FACTS Devices in Electrical Transmission System with Optimal Rescheduling of Generators by Brain Storm Optimization Algorithm,” which gives a set of examples of utilizing BSO algorithms on solving real-world optimization problems. With applications in complex engineering or design problems, the strength and limitation of various brain storm optimization algorithms could be revealed and interpreted.



This book is primarily intended for researchers, engineers, and graduate students with an interest in BSO algorithms and their applications. The chapters cover various aspects of BSO algorithms, and collectively provide broad insights into what these algorithms have to offer. The book is ideally suited as a graduate-level textbook, whereby students may be tasked with the study of the rich variants of BSO algorithms that involves a hands-on implementation to demonstrate the utility and applicability of BSO algorithms in solving optimization problems.


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