Holdings: Multi-objective swarm intelligent systems

Multi-objective swarm intelligent systems: theory & experiences

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Bibliographic Details
Other Authors:	Nedjah, Nadia (Editor)
Format:	Book
Language:	English
Published:	Berlin [u.a.] Springer 2010
Series:	Studies in computational intelligence 261
Subjects:	Mehrkriterielle Optimierung Schwarmintelligenz Aufsatzsammlung
Online Access:	Inhaltsverzeichnis
Physical Description:	XXI, 207 S. Ill., graph. Darst., Kt.
ISBN:	9783642051647

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MARC


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adam_text	CONTENTS MULTIOBJECTIVE GAUSSIAN PARTICLE SWARM APPROACH APPLIED TO MULTI-LOOP PI CONTROLLER TUNING OF A QUADRUPLE-TANK SYSTEM 1 LEANDRO DOS SANTOS COELHO, HELON VICENTE HULTMANN AY ALA, NADIA NEDJAH, LUIZA DE MACEDO MOURELLE 1.1 INTRODUCTION 2 1.2 DESCRIPTION OF QUADRUPLE-TANK PROCESS 3 1.3 FUNDAMENTALS OF MULTI-OBJECTIVE OPTIMIZATION AND PSO .... 5 1.3.1 MULTI-OBJECTIVE OPTIMIZATION 6 1.3.2 CLASSICAL PSO AND MOPSO APPROACHES 7 1.3.3 THE PROPOSED MGPSO APPROACH 9 1.4 SIMULATION RESULTS 9 1.5 SUMMARY 14 REFERENCES 14 A NON-ORDERED RULE INDUCTION ALGORITHM THROUGH MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION: ISSUES AND APPLICATIONS 17 ANDRE B. DE CARVALHO, AURORA POZO, SILVIA VERGUIO 2.1 INTRODUCTION 17 2.2 RULE LEARNING CONCEPTS 20 2.3 PERFORMANCE ASSESSMENT OF STOCHASTIC MULTI-OBJECTIVE OPTIMIZERS 23 2.3.1 PERFORMANCE ASSESSMENT 26 2.4 MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION 27 2.5 RULE LEARNING WITH MOPSO 29 2.6 MOPSO EVALUATION 31 2.6.1 AUC COMPARISON 32 2.6.2 PARETO FRONT ANALYSIS 33 BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/997432438 DIGITALISIERT DURCH X CONTENTS 2.7 PREDICTING FAULTS 35 2.7.1 DATASET 36 2.7.2 EVALUATION MEASURES 36 2.7.3 ALGORITHMS 37 2.7.4 AUC COMPARISON 37 2.7.5 INFLUENCE OF C-K SUITE METRICS IN FAULT-PRONENESS ... 37 2.8 RELATED WORKS 39 2.9 CONCLUSIONS 40 REFERENCES 41 3 USE OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS IN WATER RESOURCES ENGINEERING 45 FRANCISCO VEMCIUS FERNANDES BARROS, EDUARDO SAVIO PASSOS RODRIGUES MARTINS, LUIZ SERGIO VASCONCELOS NASCIMENTO, DIRCEU SILVEIRA REIS JR. 3.1 INTRODUCTION 45 3.2 LITERATURE REVIEW 47 3.3 EVOLUTIONARY ALGORITHM 48 3.3.1 UNI-OBJECTIVE OPTIMIZATION 49 3.3.2 MULTIOBJECTIVE APPROACH USING PARETO DOMINANCE CRITERION 54 3.4 EVALUATION OF THE ALGORITHMS WITH TEST FUNCTIONS 58 3.4.1 TEST FUNCTIONS AND THEIRS THEORETICAL MINIMA 58 3.5 USE OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS IN CALIBRATION OF HYDROLOGIE MODELS 62 3.5.1 HYDROLOGIE MODEL 63 3.5.2 DISCUSSION OF THE RESULTS 66 3.6 USE OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS IN RESERVOIRS SYSTEM OPERATION 71 3.6.1 BACKGROUND 71 3.6.2 THE RESERVOIR SYSTEM 72 3.6.3 THE CURRENT OPERATING POLICY 74 3.6.4 DERIVATION OF A NEW OPERATING POLICY 75 3.7 SUMMARY 78 REFERENCES 80 4 MICRO-MOPSO: A MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZER THAT USES A VERY SMALL POPULATION SIZE 83 JUAN CONTENTS XI 4.5.2 REINITIALIZATION PROCESS 92 4.5.3 MUTATION OPERATOR 92 4.6 EXPERIMENTS AND RESULTS 93 4.6.1 PERFORMANCE MEASURES 93 4.6.2 EXPERIMENTS 97 4.6.3 RESULTS 98 4.7 CONCLUSIONS AND FUTURE WORK 100 REFERENCES 101 DYNAMIC MULTI-OBJECTIVE OPTIMISATION USING PSO 105 MARDE GREEFF, ANDRIES P. ENGELBRECHT 5.1 INTRODUCTION 105 5.2 BACKGROUND 107 5.3 VECTOR EVALUATED PARTICLE SWARM OPTIMISATION 109 5.3.1 VEPSO FOR DYNAMIC MULTI-OBJECTIVE OPTIMISATION 110 5.4 EXPERIMENTS 110 5.4.1 BENCHMARK FUNCTIONS ILL 5.4.2 PERFORMANCE METRICS 112 5.4.3 STATISTICAL ANALYSIS 113 5.5 RESULTS 114 5.5.1 OVERALL PERFORMANCE 114 5.5.2 POPULATION SIZE 117 5.5.3 RESPONSE STRATEGIES 120 5.6 SUMMARY 121 REFERENCES 121 META-PSO FOR MULTI-OBJECTIVE EM PROBLEMS 125 MARCO MUSSETTA, PAOLA PIRINOLI, STEFANO SELLERI, RICCARDO E. ZIEH 6.1 INTRODUCTION 125 6.2 BASIC PSO ALGORITHM 130 6.3 UNDIFFERENTIATED META-PSO 132 6.3.1 META-PSO 132 6.3.2 MODIFIED META-PSO 133 6.3.3 STABILIZED MODIFIED META-PSO 134 6.4 DIFFERENTIATED META-PSO ALGORITHMS 134 6.4.1 ABSOLUTE LEADER META-PSO 134 6.4.2 DEMOCRATIC LEADER META-PSO 135 6.5 ALGORITHM TUNING 136 6.6 META-PSO FOR MULTI-OBJECTIVE OPTIMIZATION 139 6. XII CONTENTS 7 MULTI-OBJECTIVE WAVELET-BASED PIXEL-LEVEL IMAGE FUSION USING MULTI-OBJECTIVE CONSTRICTION PARTICLE SWARM OPTIMIZATION 151 YIFENG NIU, LINCHENG SHEN, XIAOHUA HUO, GUANGXIA LIANG 7.1 INTRODUCTION 152 7.2 FUNDAMENTALS OF WAVELET TRANSFORM 153 7.2.1 WAVELET TRANSFORM OF 1-D SIGNALS 154 7.2.2 WAVELET TRANSFORM OF 2-D IMAGES 155 7.3 MULTI-OBJECTIVE PIXEL-LEVEL IMAGE FUSION BASED ON DISCRETE WAVELET TRANSFORM 157 7.4 EVALUATION METRICS OF IMAGE FUSION 158 7.4.1 IMAGE FEATURE METRICS 159 7.4.2 IMAGE SIMILARITY METRICS 159 7.4.3 MUTUAL INFORMATION METRICS 160 7.5 MOCPSO ALGORITHM 162 7.5.1 MOCPSO FLOW 163 7.5.2 INITIALIZATION OF ALGORITHM 163 7.5.3 UPDATE PARTICLE SWARM 164 7.5.4 ADAPTIVE MUTATION 164 7.5.5 REPOSITORY CONTROL 165 7.5.6 UNIFORM DESIGN FOR PARAMETER ESTABLISHMENT 166 7.5.7 CONVERGENCE ANALYSIS OF MOCPSO 167 7.6 EXPERIMENTS AND ANALYSIS 168 7.6.1 UNIFORM DESIGN FOR MOCPSO PARAMETERS 169 7.6.2 COMPARISON OF MOCPSO AND MOPSO 170 7.6.3 MULTI-FOCUS IMAGE FUSION 171 7.6.4 BLIND IMAGE FUSION 173 7.6.5 MULTI-RESOLUTION IMAGE FUSION 173 7.6.6 COLOR IMAGE FUSION 175 7.7 CONCLUSIONS 176 REFERENCES 177 8 MULTI-OBJECTIVE DAMAGE IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION TECHNIQUES 179 RICARDO PERERA, SHENG-EN FANG 8. CONTENTS XIII 8.5.3 SELECTION OF PBEST AND GBEST 189 8.5.4 USE OF A MUTATION OPERATOR 190 8.6 BENCHMARKING EXPERIMENTS 191 8.6.1 SIMPLY SUPPORTED BEAM 192 8.6.2 EXPERIMENTAL REINFORCED CONCRETE FRAME 199 8.7 CONCLUSIONS 205 REFERENCES 205 AUTHOR INDEX 209 INDEX 211
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discipline	Informatik Mathematik
format	Book
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spelling	Multi-objective swarm intelligent systems theory & experiences Nadia Nedjah ... (eds.) Berlin [u.a.] Springer 2010 XXI, 207 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 261 Mehrkriterielle Optimierung (DE-588)4610682-0 gnd rswk-swf Schwarmintelligenz (DE-588)4793676-9 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Schwarmintelligenz (DE-588)4793676-9 s Mehrkriterielle Optimierung (DE-588)4610682-0 s DE-604 Nedjah, Nadia edt Studies in computational intelligence 261 (DE-604)BV020822171 261 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018703765&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Multi-objective swarm intelligent systems theory & experiences Studies in computational intelligence Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Schwarmintelligenz (DE-588)4793676-9 gnd
subject_GND	(DE-588)4610682-0 (DE-588)4793676-9 (DE-588)4143413-4
title	Multi-objective swarm intelligent systems theory & experiences
title_auth	Multi-objective swarm intelligent systems theory & experiences
title_exact_search	Multi-objective swarm intelligent systems theory & experiences
title_full	Multi-objective swarm intelligent systems theory & experiences Nadia Nedjah ... (eds.)
title_fullStr	Multi-objective swarm intelligent systems theory & experiences Nadia Nedjah ... (eds.)
title_full_unstemmed	Multi-objective swarm intelligent systems theory & experiences Nadia Nedjah ... (eds.)
title_short	Multi-objective swarm intelligent systems
title_sort	multi objective swarm intelligent systems theory experiences
title_sub	theory & experiences
topic	Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Schwarmintelligenz (DE-588)4793676-9 gnd
topic_facet	Mehrkriterielle Optimierung Schwarmintelligenz Aufsatzsammlung
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