LEADER 04216nam 22005055 450 001 9910483007203321 005 20200703133542.0 010 $a3-030-15070-4 024 7 $a10.1007/978-3-030-15070-9 035 $a(CKB)4100000008347178 035 $a(MiAaPQ)EBC5785102 035 $a(DE-He213)978-3-030-15070-9 035 $a(PPN)243769903 035 $a(EXLCZ)994100000008347178 100 $a20190603d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBrain Storm Optimization Algorithms $eConcepts, Principles and Applications /$fedited by Shi Cheng, Yuhui Shi 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (305 pages) 225 1 $aAdaptation, Learning, and Optimization,$x1867-4534 ;$v23 311 $a3-030-15069-0 327 $aBrain Storm Optimization Algorithms: More Questions than Answers -- Brain Storm Optimization for Test Task Scheduling Problem -- Oppositional Brain Storm Optimization for Fault Section Location in Distribution Networks -- Multi-objective Brain Storm Optimization Based on Differential Evolution for Environmental/Economic Dispatch Problem -- Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation -- Brain Storm Algorithm Combined with Covariance Matrix Adaptation Evolution Strategy for Optimization -- A Feature Extraction Method Based on BSO Algorithm for Flight Data -- Brain Storm Optimization Algorithms for Solving Equations Systems -- StormOptimus: A Single Objective Constrained Optimizer Based on Brainstorming Process for VLSI Circuits -- Brain Storm Optimization Algorithms for Flexible Job Shop Scheduling Problem -- Enhancement of Voltage Stability using FACTS Devices in Electrical Transmission System with Optimal Rescheduling of Generators by Brain Storm Optimization Algorithm. 330 $aBrain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process. Since the introduction of BSO algorithms in 2011, many studies on them have been conducted. They not only offer an optimization method, but could also be viewed as a framework of optimization techniques. The process employed in the algorithms could be simplified as a framework with two basic operations: the converging operation and the diverging operation. A ?good enough? optimum could be obtained through recursive solution divergence and convergence. The resulting optimization algorithm would naturally have the capability of both convergence and divergence. This book is primarily intended for researchers, engineers, and graduate students with an interest in BSO algorithms and their applications. The chapters cover various aspects of BSO algorithms, and collectively provide broad insights into what these algorithms have to offer. The book is ideally suited as a graduate-level textbook, whereby students may be tasked with the study of the rich variants of BSO algorithms that involves a hands-on implementation to demonstrate the utility and applicability of BSO algorithms in solving optimization problems. . 410 0$aAdaptation, Learning, and Optimization,$x1867-4534 ;$v23 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 676 $a006.3824 702 $aCheng$b Shi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aShi$b Yuhui$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910483007203321 996 $aBrain Storm Optimization Algorithms$92843842 997 $aUNINA