國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Brain storm optimization algorithmsc...

Cheng, Shi.

Brain storm optimization algorithmsconcepts, principles and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Brain storm optimization algorithmsedited by Shi Cheng, Yuhui Shi.
其他題名:	concepts, principles and applications /
其他作者:	Cheng, Shi.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xv, 299 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Swarm intelligence.
電子資源:	https://doi.org/10.1007/978-3-030-15070-9
ISBN:	9783030150709$q(electronic bk.)

Brain storm optimization algorithmsconcepts, principles and applications /
Brain storm optimization algorithmsconcepts, principles and applications /[electronic resource] :edited by Shi Cheng, Yuhui Shi. - Cham :Springer International Publishing :2019. - xv, 299 p. :ill., digital ;24 cm. - Adaptation, learning, and optimization,v.231867-4534 ;. - Adaptation, learning, and optimizationing, and optimization ;v.23..

Brain 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.

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.

ISBN: 9783030150709$q(electronic bk.)

Standard No.: 10.1007/978-3-030-15070-9doiSubjects--Topical Terms:

237730
Swarm intelligence.

LC Class. No.: Q337.3 / .B73 2019

Dewey Class. No.: 006.3824

Brain storm optimization algorithmsconcepts, principles and applications /
LDR:03442nmm a2200337 a 4500 001 562928
003 DE-He213
005 20191021201409.0
006 m d
007 cr nn 008maaau
008 200227s2019 gw s 0 eng d
020 $a 9783030150709$q(electronic bk.)
020 $a 9783030150693$q(paper)
024 7 $a 10.1007/978-3-030-15070-9 $2 doi
035 $a 978-3-030-15070-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q337.3 $b .B73 2019
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3824 $2 23
090 $a Q337.3 $b .B814 2019
245 0 0 $a Brain storm optimization algorithms $h [electronic resource] : $b concepts, principles and applications / $c edited by Shi Cheng, Yuhui Shi.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xv, 299 p. : $b ill., digital ; $c 24 cm.
490 1 $a Adaptation, learning, and optimization, $x 1867-4534 ; $v v.23
505 0 $a Brain 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.
520 $a 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.
650 0 $a Swarm intelligence. $3 237730
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Cheng, Shi. $3 848256
700 1 $a Shi, Yuhui. $3 340473
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Adaptation, learning, and optimizationing, and optimization ; $v v.23. $3 848257
856 4 0 $u https://doi.org/10.1007/978-3-030-15070-9
950 $a Intelligent Technologies and Robotics (Springer-42732)