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Brain Storm Optimization Algorithms

Concepts, Principles and Applications

  • Book
  • © 2019

Overview

Editors:
  1. Shi Cheng

    1. School of Computer Science, Shaanxi Normal University, Xi’an, China

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  2. Yuhui Shi

    1. Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Presents the latest advances in Brain Storm Optimization (BSO) algorithms
  • Shares new ideas and outlines future directions of development
  • Presents various concepts, principles and applications of Brain Storm Optimization algorithms

Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 23)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xv
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  2. Foundations

    1. Front Matter

      Pages 1-1
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    2. Brain Storm Optimization Algorithms: More Questions than Answers

      • Shi Cheng, Hui Lu, Xiujuan Lei, Yuhui Shi
      Pages 3-32

  3. Methodology

    1. Front Matter

      Pages 33-33
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    2. Brain Storm Optimization for Test Task Scheduling Problem

      • Hui Lu, Rongrong Zhou, Shi Cheng, Yuhui Shi
      Pages 35-59

    3. Oppositional Brain Storm Optimization for Fault Section Location in Distribution Networks

      • Guojiang Xiong, Jing Zhang, Dongyuan Shi, Yu He
      Pages 61-77

    4. Multi-objective Differential-Based Brain Storm Optimization for Environmental Economic Dispatch Problem

      • Yali Wu, Xinrui Wang, Yingruo Xu, Yulong Fu
      Pages 79-104

    5. Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation

      • Seham Elsayed, Mohammed El-Abd, Karam Sallam
      Pages 105-122

    6. Brain Storm Algorithm Combined with Covariance Matrix Adaptation Evolution Strategy for Optimization

      • Yang Yu, Lin Yang, Yirui Wang, Shangce Gao
      Pages 123-154

  4. Applications

    1. Front Matter

      Pages 155-155
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    2. A Feature Extraction Method Based on BSO Algorithm for Flight Data

      • Hui Lu, Chongchong Guan, Shi Cheng, Yuhui Shi
      Pages 157-188

    3. Brain Storm Optimization Algorithms for Solving Equations Systems

      • Liviu Mafteiu-Scai, Emanuela Mafteiu, Roxana Mafteiu-Scai
      Pages 189-220

    4. StormOptimus: A Single Objective Constrained Optimizer Based on Brainstorming Process for VLSI Circuits

      • Satyabrata Dash, Deepak Joshi, Sukanta Dey, Meenali Janveja, Gaurav Trivedi
      Pages 221-243

    5. Brain Storm Optimization Algorithms for Flexible Job Shop Scheduling Problem

      • Xiuli Wu
      Pages 245-271

    6. Enhancement of Voltage Stability Using FACTS Devices in Electrical Transmission System with Optimal Rescheduling of Generators by Brain Storm Optimization Algorithm

      • G. V. Nagesh Kumar, B. Sravana Kumar, B. Venkateswara Rao, D. Deepak Chowdary
      Pages 273-297

  5. Back Matter

    Pages 299-299
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Keywords

About this book

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.


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.   
 



Editors and Affiliations

  • School of Computer Science, Shaanxi Normal University, Xi’an, China

    Shi Cheng

  • Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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