عنوان

Brain Storm Optimization Algorithms :

شابک

3030150704

شابک

3030150712

شابک

3030150720

شابک

9783030150709

شابک

9783030150716

شابک

9783030150723

شابک اشتباه

3030150690

شابک اشتباه

9783030150693

عنوان اصلي

Brain Storm Optimization Algorithms :

نام عام مواد

[Book]

ساير اطلاعات عنواني

Concepts, Principles and Applications /

نام نخستين پديدآور

Shi Cheng, Yuhui Shi, editors.

محل نشرو پخش و غیره

Cham, Switzerland :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

[2019]

نام خاص و کميت اثر

1 online resource (xiii, 298 pages)

عنوان فروست

Adaptation, Learning, and Optimization ;

مشخصه جلد

v. 23

متن يادداشت

2.1 Brain Storm Optimization

متن يادداشت

Intro; Preface; Contents; Contributors; Acronyms; Part I Foundations; Brain Storm Optimization Algorithms: More Questions than Answers; 1 Introduction; 2 Nature-Inspired Intelligent Algorithms; 2.1 Evolutionary Computation; 2.2 Swarm Intelligence; 3 Brain Storm Optimization; 3.1 Brainstorming Process Model; 3.2 Brain Storm Optimization Algorithms; 4 Variants of BSO Algorithms; 4.1 Theory Analysis; 4.2 New Individual Generation Operation; 4.3 Distribution Function; 4.4 Transfer Function; 4.5 Hybrid Algorithms; 5 Problems Solving; 5.1 Single Objective Problems; 5.2 Multi-objective Optimization

متن يادداشت

4 Brain Storm Optimization Algorithm Based on Differential Evolution4.1 Brain Storm Optimization Algorithm; 4.2 Brain Storm Optimization Algorithm Based on Differential Evolution; 5 Multi-objective Differential-Based Brain Storm Optimization Algorithm (MDBSO); 6 MDBSO Algorithm for Environmental/Economic Dispatch; 7 Experiments and Discussions; 7.1 Test System 1; 7.2 Test System 2; 8 Conclusions; References; Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation; 1 Introduction; 2 Literature Review and Related Work

متن يادداشت

4.1 BSO Based Single Objective Test Task Scheduling Algorithm4.2 BSO Based Multi-objective Test Task Scheduling Algorithm; 5 Experimental Study; 5.1 Parameters Setting; 5.2 Experiment for Single Objective TTSP; 5.3 Experiment for Multi-objective TTSP; 6 Conclusion; References; Oppositional Brain Storm Optimization for Fault Section Location in Distribution Networks; 1 Introduction; 2 Problem Formulation; 2.1 Objective Function; 2.2 Switching Function; 3 Brain Storm Optimization in Objective Space; 4 Oppositional Brain Storm Optimization; 4.1 Opposition-Based Learning

متن يادداشت

4.2 Oppositional Brain Storm Optimization5 Application of OBSO for FSL; 6 Case Studies; 6.1 Test System; 6.2 Simulation Settings; 6.3 Performance Criteria; 6.4 Simulation Results and Comparison; 7 Conclusions and Future Work; References; Multi-objective Differential-Based Brain Storm Optimization for Environmental Economic Dispatch Problem; 1 Introduction; 2 The Formulation of Environmental Economic Dispatch Problem; 2.1 The Objective Function of EED Problem; 2.2 The Constraints of EED Problem; 3 Multi-objective Optimization Problem (MOP)

متن يادداشت

5.3 Multimodal Optimization5.4 Real-World Applications; 6 Future Research; 6.1 Unified Swarm Intelligence; 6.2 Evolution and Learning; 6.3 Real-World Applications; 7 Conclusion; References; Part II Methodology; Brain Storm Optimization for Test Task Scheduling Problem; 1 Introduction; 2 Related Work; 2.1 Test Task Scheduling Problem (TTSP); 2.2 Brain Storm Optimization; 2.3 Multi-objective Optimization Algorithms; 3 Test Task Scheduling Problem; 3.1 The Mathematical Programming Model for TTSP; 3.2 Characteristics Analysis; 3.3 Encoding Strategy; 4 BSO Based Test Task Scheduling Algorithm

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

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 â#x80;good enoughâ#x80;#x9D; 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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783030150709

عنوان

Brain Storm Optimization Algorithms : Concepts, Principles and Applications.

شماره استاندارد بين المللي کتاب و موسيقي

9783030150693

موضوع مستند نشده

Swarm intelligence.

موضوع مستند نشده

Swarm intelligence.

موضوع مستند نشده

COM004000

موضوع مستند نشده

UYQ

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Cheng, Shi

مستند نام اشخاص تاييد نشده

Shi, Yuhui

تاريخ عمليات

20200823084204.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y