Atjaunināt sīkdatņu piekrišanu

E-grāmata: Swarm Intelligence: Applications, Volume 3

Edited by (Peking University, Computational Intelligence Laboratory, China)
  • Formāts: EPUB+DRM
  • Sērija : Control, Robotics and Sensors
  • Izdošanas datums: 09-Oct-2018
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781785616327
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 237,94 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Swarm Intelligence: Applications, Volume 3Palielināt
  • Formāts: EPUB+DRM
  • Sērija : Control, Robotics and Sensors
  • Izdošanas datums: 09-Oct-2018
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781785616327
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

The concept of swarm intelligence at first originated from the observation of nature. Through the observation and study of the behaviour of swarms of living creatures as ants colony, bird flocks, bees colony and fish school, inspired by the swarm/social phenomena exhibited by these biological swarms, the swarm of simple individuals through mutual cooperation shows up the emergence phenomena at the level of swarm, that is, 'the swarm of simple individuals shows the characteristics of complex intelligent behaviour through cooperation.'

The swarm intelligence algorithms are characterised of simplicity, uncertainty, interactivity, distributed parallelism, robustness, scalability, and self-organisation. At present, the study of swarm intelligence algorithms mainly includes theory, algorithm and application. Its development trends include developing hybrid algorithms, new improved algorithms and theoretical analysis as well as solving large-scale problems (big data application). In general, swarm intelligence algorithms may shed a light on breaking the curse of no free lunches (NFLs), which shows that a deep study might give us enough anticipation motivating more and more researchers to engage in the research of swarm intelligence algorithms and their applications.

Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will get inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.

Volume 3 includes 27 chapters and presents a great number of real-world applications of swarm intelligence algorithms and related evolutionary algorithms.

With contributions from an international selection of leading researchers, Swarm Intelligence is essential reading for engineers, researchers, professionals and practitioners with interests in swarm intelligence working in the fields of computer science, information technology, artificial intelligence, neural networks, computational intelligence, bioengineering, physics, mathematics, and social sciences.



This book includes 27 chapters and presents a great number of real-world applications of swarm intelligence algorithms and related evolutionary algorithms.

Preface xxi
About the editor xxxiii
List of technical reviewers
xxxv
List of additional reviewers
xxxvii
Acknowledgements xxxix
1 Prototype generation based on MOPSO
1(1)
Weiwei Hu
Ying Tan
Abstract
1(1)
1.1 Introduction
1(2)
1.2 Related work
3(3)
1.3 Particle swarm optimization and its application to prototype generation
6(1)
1.3.1 Framework of PSO
6(1)
1.3.2 Multiobjective PSO
7(1)
1.3.3 PSO for prototype generation
8(1)
1.4 Error rank
9(1)
1.4.1 Motivation of error rank
9(1)
1.4.2 Definition of error rank
10(2)
1.5 Multiobjective optimization strategy for learning
12(2)
1.6 Experiments
14(1)
1.6.1 Experimental setup
14(1)
1.6.2 Comparison algorithms
15(1)
1.6.3 Hypothesis test
16(1)
1.6.4 Experimental results
17(7)
1.6.5 Comparison with 28 prototype generation algorithms on the 59 datasets offered by Triguero et al.
24(2)
1.7 Conclusions
26(1)
References
26(5)
2 Image reconstruction algorithms for electrical impedance tomography based on swarm intelligence
31(1)
Wellington Pinheiro dos Santos
Ricardo Emmanuel de Souza
Reiga Ramalho Ribeiro
Allan Rivalles Souza Feitosa
Valter Augusto de Freitas Barbosa
Victor Luiz Bezerra Araujo da Silva
David Edson Ribeiro
Rafaela Covello de Freitas
Abstract
31(1)
2.1 Introduction
32(1)
2.2 Related work
33(1)
2.3 Swarm intelligence
34(1)
2.3.1 Particle swarm optimization
34(2)
2.3.2 Fish school search
36(3)
2.3.3 Artificial bee colony
39(1)
2.4 Application: electrical impedance tomography
40(1)
2.4.1 Mathematical modeling of EIT problems
40(1)
2.4.2 Direct and inverse problem
41(1)
2.4.3 EIT reconstruction method as an optimization problem
42(2)
2.4.4 Ground-truth images
44(1)
2.4.5 Computational solutions obtained from swarm techniques
44(6)
2.4.6 Hardware proposal
50(2)
References
52(5)
3 A semisupervised fuzzy GrowCut algorithm for segmenting masses of regions of interest of mammography images
57(1)
Filipe R. Cordeiro
Wellington P. Santos
Abel G. Silva-Filho
Abstract
57(1)
3.1 Introduction
57(1)
3.2 Related work
58(2)
3.3 Materials and methods
60(1)
3.3.1 Fuzzy GrowCut model
60(6)
3.3.2 Automatic selection of seeds
66(1)
3.3.3 Adaptive selection of parameters
67(1)
3.3.4 Methodology
68(1)
3.3.5 Experimental environment
69(1)
3.3.6 Metrics
70(2)
3.4 Results
72(1)
3.4.1 Fault tolerance analysis
72(2)
3.4.2 General results
74(6)
3.5 Conclusion
80(1)
References
81(4)
4 Multiobjective optimization of autonomous control for a biped robot
85(1)
M.J. Mahmoodabadi
M. Taher Khorsandi
S.E. Rasouli
Abstract
85(1)
4.1 Introduction
85(2)
4.2 Optimization algorithm
87(1)
4.2.1 Particle swarm optimization
87(1)
4.2.2 Convergence operator
87(1)
4.2.3 Divergence operator
88(1)
4.2.4 Combination of PSO, convergence, and divergence operators
88(1)
4.2.5 Multiobjective procedure
89(1)
4.3 Biped robot
90(2)
4.3.1 Dynamic model and inverse kinematic
92(4)
4.3.2 Proportional--derivative control
96(2)
4.4 Multiobjective optimal control of the biped robot
98(7)
4.5 Conclusion
105(1)
References
105(4)
5 Swarm intelligence based MIMO detection techniques in wireless systems
109(1)
Adrian Ahmed Khan
Zakir Ullah
Abstract
109(1)
5.1 Introduction
109(1)
5.2 System model
110(1)
5.3 Problem formulation
111(1)
5.4 Existing MIMO detectors
112(1)
5.4.1 Linear detection
112(1)
5.4.2 Nonlinear detectors
113(1)
5.5 Nature-inspired optimization techniques
114(1)
5.5.1 Genetic algorithm
114(1)
5.5.2 Particle swarm optimization
114(1)
5.5.3 Ant colony optimization
114(2)
5.6 Genetic algorithm based detection for MIMO techniques
116(1)
5.6.1 Initialization of GA
116(1)
5.6.2 Fitness evaluation using cost function
116(1)
5.6.3 Optimality test
117(1)
5.6.4 Selection
117(1)
5.6.5 Reproduction
117(1)
5.6.6 Crossover
117(1)
5.6.7 Mutation
118(1)
5.6.8 Decisionmaking
118(1)
5.6.9 Performance analysis
118(2)
5.7 MIMO detection using particle swarm optimization
120(1)
5.7.1 PSO-MI MO detection algorithm
120(1)
5.7.2 SPSO-MIMO detection algorithm
121(1)
5.7.3 MPSO-MIMO detection algorithm
122(1)
5.7.4 MIMO detection algorithm based on binary PSO (BPSO-MIMO)
122(1)
5.7.5 Control of PSO parameters
123(1)
5.7.6 Performance analysis of PSO-based MIMO detection techniques
123(1)
5.7.7 MIMO-PSO detection algorithms' BER performance
123(5)
5.7.8 Behavior with increase in iterations
128(1)
5.7.9 Effect of parameters adjustment in system behavior
128(1)
5.7.10 Analysis of MIMO-PSO as an effective MIMO detector
128(1)
5.8 MIMO detection using ant colony optimization (ACO) algorithm
129(1)
5.8.1 Binary ant system
130(1)
5.8.2 BA-MIMO detection algorithm
131(1)
5.8.3 Performance evaluation of BA-MIMO detection
131(2)
5.8.4 Computational complexity comparison
133(1)
5.8.5 Performance complexity trade-off
134(1)
5.9 Applications of SI in MIMO detection
135(1)
5.10 Conclusion
135(1)
References
135(4)
6 Swarm intelligence in logistics and production planning
139(1)
Thomas Hanne
Suash Deb
Simon Fong
Abstract
139(1)
6.1 Introduction
139(2)
6.2 A brief overview of metaheuristics and swarm intelligence
141(1)
6.2.1 Categorization of metaheuristics
141(2)
6.2.2 Swarm algorithms and optimization problems
143(1)
6.2.3 Some selected swarm algorithms
144(4)
6.3 Optimization problems in production and logistics
148(1)
6.3.1 Economic order quantities and lot sizes
148(3)
6.3.2 Scheduling problems
151(3)
6.3.3 Vehicle routing problems
154(5)
6.4 Conclusions
159(2)
References
161(6)
7 Swarm intelligence for object-based image analysis
167(1)
Fatemeh Tabib Mahmoudi
Abstract
167(1)
7.1 Introduction
168(1)
7.2 Object-based image analysis
169(2)
7.2.1 Spectral features
171(1)
7.2.2 Textural features
171(1)
7.2.3 Structural features
172(1)
7.2.4 Contextual features
173(1)
7.3 Optimum feature/parameter selection
174(1)
7.4 Optimization algorithm
174(5)
7.4.1 Ant colony optimization
175(1)
7.4.2 Particle swarm optimization
176(1)
7.4.3 Firefly swarm optimization
177(2)
7.5 Optimum feature selection based on swarm intelligence
179(1)
7.6 Experimental results of swarm-based optimum feature selection
180(6)
7.6.1 Data set
180(2)
7.6.2 Evaluation function and accuracy assessment
182(4)
7.7 Conclusion
186(1)
References
186(3)
8 Evolutionary multiobjective optimization for multilabel learning
189(1)
Chuan Shi
Abstract
189(1)
8.1 Multiobjective multilabel classification
189(1)
8.1.1 Overview
189(3)
8.1.2 Related work
192(1)
8.1.3 Problem definition
193(1)
8.1.4 The MOML algorithm
194(6)
8.1.5 Experiments
200(5)
8.2 Multilabel ensemble learning
205(1)
8.2.1 Overview
205(2)
8.2.2 The EnML method
207(3)
8.2.3 Experiments
210(6)
8.3 Conclusion
216(1)
References
216(3)
9 Image segmentation by flocking-like particle dynamics
219(1)
Roberto Alves Gueleri
Qiusheng Zheng
Junbao Zhang
Liang Zhao
Abstract
219(1)
9.1 Introduction
219(2)
9.2 Related work
221(1)
9.3 Model description
222(2)
9.4 Experimental results
224(1)
9.4.1 Overview of achievable results
224(2)
9.4.2 Parameter analysis
226(9)
9.4.3 Comparative results
235(6)
9.5 Conclusions
241(1)
Acknowledgement
242(1)
References
242(3)
10 Swarm intelligence for controller tuning and control of fractional systems
245(1)
Zafer Bingul
Oguzhan Karahan
Abstract
245(1)
10.1 Introduction
245(3)
10.2 Swarm-based optimization algorithms
248(1)
10.3 Particle swarm optimization
248(2)
10.4 Artificial bee colony
250(3)
10.5 Cuckoo search algorithm
253(2)
10.6 Ant colony optimization algorithm
255(2)
10.7 Fractional calculus and fractional order PID controller
257(3)
10.8 Simulation results and discussion
260(2)
10.9 Case study 1: lag-dominated FOS
262(5)
10.10 Case study 2: delay-dominated FOS
267(5)
10.11 Case study 3: high-order complex delay FOS
272(4)
10.12 Conclusion
276(1)
References
277(6)
11 PSO-based implementation of smart antennas for secure localisation
283(1)
Rathindra Nath Biswas
Anurup Saha
Swarup Kumar Mitra
Mrinal Kanti Naskar
Abstract
283(1)
11.1 Introduction
283(2)
11.2 Related works
285(2)
11.3 Overview of the proposed scheme
287(1)
11.3.1 Security challenges in wireless localisation system
287(1)
11.3.2 Smart antennas preliminaries
288(2)
11.3.3 PSO algorithm outline
290(1)
11.3.4 PSO-based smart antenna design methodology
291(13)
11.4 Hardware realisation of smart antennas
304(1)
11.4.1 PCB fabrication of the optimised patch antenna
304(1)
11.4.2 Implementation of adaptive beamformer on FPGA devices
305(3)
11.5 System performance analysis
308(1)
11.5.1 Simulation and measurement on optimised patch antenna
308(6)
11.5.2 Simulation and experimentation on adaptive beamformer
314(8)
11.6 Conclusions and future scope
322(1)
References
323(4)
12 Evolutionary computation for NLP tasks
327(1)
Ana Paula Silva
Arlindo Silva
Abstract
327(1)
12.1 Introduction
327(1)
12.2 Global optimisation
328(2)
12.3 Evolutionary computation
330(6)
12.4 Evolutionary algorithms
336(1)
12.4.1 Genetic algorithms
337(1)
12.4.2 Evolutionary strategies
337(2)
12.4.3 Evolutionary programming
339(1)
12.4.4 Genetic programming
339(1)
12.5 Swarm intelligence
340(1)
12.5.1 Particle swarm optimisation
341(1)
12.5.2 Ant colony optimisation
342(1)
12.6 Evolutionary computation in natural language processing tasks
343(1)
12.6.1 Word segmentation
343(1)
12.6.2 Part-of-speech tagging
344(9)
12.6.3 Syntactic sentence analysis
353(6)
12.6.4 Grammar generation
359(3)
12.7 Final considerations
362(1)
References
363(6)
13 Particle swarm optimisation for antenna element design
369(1)
Waroth Kuhirun
Winyou Silabut
Pravit Boonek
Abstract
369(1)
13.1 Introduction
369(1)
13.2 Particle swarm optimisation
370(1)
13.2.1 Real-number particle swarm optimisation
371(2)
13.2.2 Binary particle swarm optimisation
373(3)
13.2.3 Hybrid real-binary particle swarm optimisation
376(1)
13.3 The application of particle swarm optimisation to antenna element design
377(5)
13.3.1 Parameterisation and coding
378(1)
13.3.2 Antenna element characteristics
379(2)
13.3.3 Fitness function and mechanism of optimisation
381(1)
13.4 Examples of horn antenna designs optimised using particle swarm optimisation
382(1)
13.4.1 Practical horn antenna design
383(1)
13.4.2 Motivation for optimisation
384(3)
13.4.3 Design of horn antennas optimised using particle swarm optimisation
387(6)
13.5 Example of a microstrip antenna design optimised using particle swarm optimisation
393(1)
13.5.1 Practical microstrip antenna design
393(1)
13.5.2 Motivation for optimisation
393(3)
13.5.3 Design of a microstrip antenna optimised using particle swarm optimisation
396(4)
13.6 Conclusion
400(1)
Acknowledgements
400(1)
References
400(3)
14 Swarm intelligence for data mining classification tasks: an experimental study using medical decision problems
403(1)
Jose A. Saez
Emilio Corchado
Abstract
403(1)
14.1 Introduction
403(2)
14.2 Using rules for classification tasks
405(1)
14.2.1 Classification and rules: basic concepts
405(1)
14.2.2 Creating rules by swarm intelligence optimization
406(2)
14.3 Particle swarm optimization in classifier building
408(2)
14.3.1 PSO algorithm to generate classification rules
410(3)
14.4 Ant colony optimization for classifier building
413(3)
14.4.1 ACO algorithm to generate classification rules
416(1)
14.5 Extracting classification rules from medical problems with swarm intelligence methods
417(1)
14.5.1 Medical decision support systems
418(1)
14.5.2 Medical datasets
419(1)
14.5.3 Experimentation and analysis of results
420(4)
14.6 Conclusions
424(1)
Acknowledgment
424(1)
References
425(4)
15 Towards spiking neural systems synthesis
429(1)
Abhaya C. Kammara S.
S. Pontes-Filho
Andreas Konig
Abstract
429(1)
15.1 Introduction
429(2)
15.2 State of the art
431(1)
15.3 ABSYNTH architecture and nonlinear circuit synthesis
432(3)
15.4 Neuromorphic spiking circuits and systems
435(1)
15.4.1 Sensor-to-spike-to-digital-converter
435(1)
15.4.2 Cost function design
436(6)
15.5 Optimization algorithms
442(4)
15.6 Experiments and results
446(1)
15.6.1 Results
447(7)
15.7 Statistical analysis
454(1)
15.7.1 Design centering (DC) for adaptive circuits
454(4)
15.7.2 Design centering (DC) for evolvable hardware
458(1)
15.8 Conclusion
458(1)
References
459(4)
16 Particle swarm optimization based memetic algorithms framework for scheduling of central planned and distributed flow shops
463(1)
Yixin Yang
Xiaoyi Feng
Bin Xin
Mengchen Ji
Xiying Du
Ling Wang
Hongjun Zhang
Bo Liu
Abstract
463(1)
16.1 Introduction
464(1)
16.2 Related work
465(1)
16.2.1 Permutation flowshop scheduling problems
465(1)
16.2.2 Particle swarm optimization
466(1)
16.2.3 Memetic algorithm
467(1)
16.3 Permutation flowshop scheduling problem
468(1)
16.4 A unified particle swarm optimization-based memetic algorithms framework for scheduling
469(1)
16.4.1 Solution representation and evaluation
469(1)
16.4.2 Population initialization
470(1)
16.4.3 PSO-based global search
471(1)
16.4.4 Local search techniques
471(4)
16.5 Numerical test and comparisons
475(1)
16.5.1 Experimental setup
475(1)
16.5.2 Simulation and comparisons
475(4)
16.6 PSO-based MA for no-wait distributed assembly permutation flowshop problem
479(1)
16.6.1 Distributed assembly permutation flowshop problem with no-wait constraint
479(2)
16.6.2 PSO-based MA for DAPFSP-NW
481(2)
16.6.3 Experimental study
483(2)
16.7 PSO-based MA for stochastic distributed assembly permutation flowshop problem
485(3)
16.7.1 Stochastic DAPFSP-NW
485(1)
16.7.2 PM-HT for stochastic DAPFSP-NW
485(2)
16.7.3 Experimental study
487(1)
16.8 Conclusion
488(1)
Acknowledgments
489(1)
References
489(6)
17 Particle swarm optimization for antenna array synthesis, diagnosis and healing
495(1)
Om Prakash Acharya
Amalendu Patnaik
Abstract
495(1)
17.1 Introduction
495(3)
17.2 Antenna array
498(1)
17.2.1 Array factor
498(1)
17.2.2 N-element linear array
498(3)
17.3 Introduction to PSO
501(4)
17.4 Test antenna array
505(1)
17.5 Array pattern synthesis using PSO
506(1)
17.5.1 Problem formulation and implementation
506(5)
17.5.2 Concluding remark
511(1)
17.6 Diagnosis of the failed array using PSO
512(1)
17.6.1 Problem formulation and implementation
513(4)
17.6.2 Concluding remark
517(1)
17.7 Healing of the failed array in PSO
517(1)
17.7.1 Problem formulation and implementation
517(5)
17.7.2 Null recovery
522(5)
17.7.3 Concluding remark
527(1)
17.8 Conclusion
527(1)
References
527(4)
18 Designing a fuzzy logic controller with particle swarm optimisation algorithm
531(1)
Gurcan Lokman
Vedat Topuz
Ahmet Fevzi Baba
Abstract
531(1)
18.1 TRIG A Mark-H reactor
531(2)
18.1.1 Literature review
533(1)
18.2 Particle swarm optimisation
534(1)
18.2.1 Basic PSO algorithm
535(2)
18.3 Fuzzy logic controller
537(1)
18.3.1 Structure of FLC
537(1)
18.4 Realised controller
538(1)
18.4.1 Trajectory
538(1)
18.4.2 Designed fuzzy controller (TTFLC)
539(2)
18.4.3 Coding the controller parameters for the PSO algorithm
541(1)
18.4.4 Determining limit values of FLC parameters
541(1)
18.4.5 PSO algorithm parameters
542(2)
18.4.6 PSO-TTFLC simulator's graphical user interface (GUI)
544(2)
18.5 Controller performance results
546(1)
18.5.1 Effect of initial power levels
546(1)
18.5.2 Effect of desired power levels
546(2)
18.5.3 Effect of period values
548(1)
18.5.4 Effect of reactivity imports
549(2)
18.5.5 Effect of transitive trajectory
551(2)
18.6 Conclusion
553(1)
References
554(3)
19 Adding swarm intelligence for slope stability analysis
557(1)
Walter W. Chen
Zhe-Ping Shen
Abstract
557(1)
19.1 Introduction and chapter outline
557(1)
19.2 Review of literature
558(1)
19.2.1 Combination of PSO and slope stability analysis
559(1)
19.2.2 Application of laser scanning
560(1)
19.3 Research method and analysis framework
561(1)
19.3.1 Scripting STABL
561(2)
19.3.2 Developing scripts
563(1)
19.4 Examples of analysis
563(1)
19.4.1 Theoretical soil profile
563(6)
19.4.2 Soil profile of a real landslide
569(8)
19.5 Summary and conclusion
577(1)
References
578(3)
20 Software module clustering using particle swarm optimization
581(1)
Amarjeet Prajapati
Jitender Kumar Chhabra
Abstract
581(1)
20.1 Introduction
581(2)
20.2 Related work
583(3)
20.3 Clustering algorithms for SMCPs
586(1)
20.3.1 GA-based software module clustering
586(1)
20.3.2 HC-based software module clustering
587(1)
20.3.3 SA-based software module clustering
587(1)
20.3.4 Particle swarm optimization
588(1)
20.4 Proposed approach
589(1)
20.4.1 Generation of MDG
590(2)
20.4.2 Particle representation and initialization
592(1)
20.4.3 Particle fitness function
592(1)
20.4.4 Particle status updating rules
592(1)
20.5 Experimental study
593(1)
20.5.1 Collecting results
593(4)
20.6 Conclusions and future works
597(1)
References
598(5)
21 A swarm intelligence approach to harness maximum techno-commercial benefits from smart power grids
603(1)
Sandip Chanda
Abhinandan De
Abstract
603(1)
21.1 Evolution of vertically integrated power system to deregulated structure and smart power grids
603(3)
21.1.1 Paradigm shift in power system optimization methods
606(4)
21.2 Resources and components of deregulated power systems and smart power grids
610(1)
21.2.1 Reliability
610(1)
21.2.2 Flexibility in network topology
611(1)
21.2.3 Efficiency
611(1)
21.2.4 Load adjustment/load balancing
612(1)
21.2.5 Peak curtailment
612(1)
21.2.6 Sustainability
612(1)
21.2.7 Market-enabling
613(1)
21.2.8 Demand response support
613(1)
21.3 Market structure in smart grid
614(1)
21.4 Modeling and computer simulation of smart grid
615(1)
21.4.1 The state variables in smart grid infrastructure
615(2)
21.5 The social welfare optimization problem
617(1)
21.5.1 Social welfare optimization---Indian scenario
617(1)
21.5.2 Social welfare optimization---international scenario
617(1)
21.6 Application of stochastic optimization algorithms in power system optimization problems
618(5)
21.6.1 Classical optimization technique
619(1)
21.6.2 Particle swarm optimization technique
620(3)
21.6.3 Application of stochastic optimization algorithms on system model
623(1)
21.7 Development of swarm intelligence based social welfare optimization algorithm
623(1)
21.7.1 Development of the objective function
624(1)
21.7.2 A novel load curtailment strategy
624(1)
21.7.3 Operational constraints
625(1)
21.7.4 The price equilibrium problem
626(1)
21.7.5 Description of the methodology
626(1)
21.7.6 Illustrative case studies and comparison with traditional optimization algorithms
627(1)
21.7.7 Base case
627(3)
21.7.8 Performance evaluation of the developed algorithm with intermittent renewable energy sources
630(3)
21.8 Summary and conclusions
633(1)
References
634(5)
22 Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation
639(1)
Kamal Z. Zamli
Bestoun S. Ahmed
Thair Mahmoud
Wasif Afzal
Abstract
639(1)
22.1 Introduction
640(1)
22.2 Combinatorial interaction testing
641(1)
22.2.1 Preliminaries
641(1)
22.2.2 Motivating example
642(2)
22.3 Related work
644(2)
22.4 PSO performance monitoring
646(1)
22.5 The strategy
647(1)
22.5.1 Fuzzy adaptive swarm VS-CIT
647(3)
22.5.2 The pair generation algorithm
650(3)
22.6 Empirical evaluation
653(1)
22.7 Observation
654(4)
22.8 Conclusions
658(1)
References
659(4)
23 Multiobjective swarm optimization for operation planning of electric power systems
663(1)
Rene Cruz Freire
Vitor Hugo Ferreira
Renan Silva Maciel
Abstract
663(1)
23.1 Introduction
663(2)
23.2 Objective
665(1)
23.3 Optimal power flow and static security analysis
666(1)
23.3.1 Formulation of the problem
667(1)
23.3.2 Security constrained optimal power flow
668(1)
23.3.3 Formulation of the SCOPF problem
669(1)
23.4 Multiobjective optimization
670(1)
23.4.1 Pareto-optimal solutions
671(1)
23.4.2 Goals in multiobjective optimization
672(1)
23.4.3 Differences in the monoobjective optimization
673(1)
23.5 Multiobjective swarm and evolutionary optimization
673(1)
23.5.1 Non-dominated sorting genetic algorithm
674(3)
23.5.2 Multiobjective evolutionary particle swarm optmization
677(2)
23.6 Problem formulation
679(1)
23.6.1 Mathematical modeling
680(2)
23.6.2 Algorithms implemented for SCOPF resolution
682(3)
23.7 Tests and simulation
685(3)
23.7.1 Chromosome encoding
688(1)
23.7.2 Results
688(12)
23.8 Conclusion
700(1)
References
701(4)
24 Perturbed-attractor particle swarm optimization for image restoration
705(1)
Deepak Devicharan
Kishan G. Mehrotra
Chilukuri K. Mohan
Pramod K. Varshney
Abstract
705(1)
24.1 Introduction
705(1)
24.2 Problem description
706(1)
24.3 Perturbed-attractor PSO for image restoration
707(5)
24.4 Restoration quality metrics
712(3)
24.4.1 Edge sharpness metric
713(1)
24.4.2 Image fidelity: mutual information
714(1)
24.4.3 Image fidelity: VIF metric
715(1)
24.5 Simulation results
715(6)
24.6 Conclusion
721(2)
Acknowledgments
723(1)
Image appendix
723(1)
References
723(2)
25 Application of swarm intelligence algorithms to multi-objective distributed local area network topology design problem
725(1)
Salman A. Khan
Amjad Mahmood
Abstract
725(1)
25.1 Introduction
725(1)
25.2 Multi-objective optimization
726(1)
25.3 Approaches for handling multiple objectives
727(1)
25.4 Brief overview of ant colony optimization, particle swarm optimization, and artificial bee colony algorithms
728(1)
25.4.1 Ant colony optimization
728(1)
25.4.2 Particle swarm optimization
729(1)
25.4.3 Artificial bee colony
730(1)
25.5 Distributed local area network topology design problem
731(1)
25.5.1 Design objectives
731(2)
25.5.2 Constraints
733(1)
25.6 Goal programming approach for the DLAN topology design problem
733(4)
25.6.1 Defining the goals
734(1)
25.6.2 Calculation of membership functions
734(2)
25.6.3 Calculation of deviational variables
736(1)
25.6.4 Formulation of the fitness function
736(1)
25.7 Swarm intelligence algorithms for DLAN topology design problem
737(1)
25.7.1 Solution structure
737(1)
25.7.2 Goal programming based ant colony optimization algorithm
738(1)
25.7.3 Goal programming based particle swarm optimization algorithm
739(2)
25.7.4 Goal programming based artificial bee colony algorithm
741(3)
25.7.5 Evolutionary artificial bee colony optimization
744(2)
25.8 Results and discussion
746(3)
25.9 Concluding remarks
749(1)
Acknowledgement
749(1)
References
749(6)
26 Swarm intelligence algorithms for antenna design and wireless communications
755(1)
Sotirios K. Goudos
Abstract
755(1)
26.1 Introduction
755(1)
26.2 Swarm intelligence algorithms
756(1)
26.2.1 Initialization
756(1)
26.2.2 Inertia weight particle swarm optimization
757(1)
26.2.3 Constriction factor particle swarm optimization
758(1)
26.2.4 Comprehensive learning particle swarm optimizer
758(1)
26.2.5 PSO for discrete-valued problems
759(2)
26.2.6 Artificial bee colony algorithm
761(1)
26.2.7 Gbest-guided ABC
762(1)
26.2.8 Ant colony optimization
762(1)
26.3 Applications, numerical examples
763(2)
26.3.1 Linear antenna array synthesis with sidelobe level suppression and null control
765(5)
26.3.2 Thinned array design
770(4)
26.3.3 PAPR reduction of OFDM signals
774(6)
26.4 Conclusion
780(1)
References
780(5)
27 Finite-element model updating using swarm intelligence algorithms
785(1)
Ilyes Boulkaibet
Fernando Buarque de Lima Neto
Tshilidzi Marwala
Bhekisipho Twala
Abstract
785(1)
27.1 Introduction
785(2)
27.2 Finite-element method
787(2)
27.3 Finite-element model updating methods
789(1)
27.4 The objective function
790(1)
27.5 Particle swarm optimization
791(1)
27.5.1 Updating a numerical example using PSO algorithm
792(2)
27.6 The ant colony optimization (ACO) algorithm for continuous domain
794(2)
27.6.1 Updating a numerical example using ACO algorithm
796(1)
27.7 Fish school search
797(3)
27.7.1 Updating a numerical example using the FSS algorithm
800(1)
27.8 The unsymmetrical H-shaped structure
801(5)
27.8.1 Simulation settings
802(1)
27.8.2 The updating results for the unsymmetrical H-shaped structure
803(3)
27.9 Finite-element model updating using a multiobjective PSO algorithm
806(1)
27.9.1 Multiobjective PSO algorithm
807(1)
27.9.2 Updating a numerical example using the MOPSO algorithm
808(2)
27.10 Conclusion
810(1)
References
810(5)
Author Index 815(2)
Subject Index 817
Ying Tan is a full professor, PhD advisor, and director of the Computational Intelligence Laboratory at Peking University, China. He is also a professor at the Faculty of Design, Kyushu University, Japan. He serves as Editor-in-Chief of the International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), and is Associate Editor of IEEE Transactions on Evolutionary Computation (TEC), IEEE Transactions on Cybernetics (CYB), IEEE Transactions on Neural Networks and Learning Systems (NNLS), International Journal of Swarm Intelligence Research (IJSIR), and International Journal of Artificial Intelligence (IJAI). He has been the founder general chair of the ICSI International Conference series since 2010, is the inventor of the Fireworks Algorithm (FWA), and has published extensively in this field.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97817856163276e.html
Keywords: