Brain Storm Optimization Algorithms

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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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.   
 

• Computational Intelligence
• Evolutionary Computation
• Swarm Intelligence
• Brain Storm Optimization
• Optimization
• BSO

• 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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