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Multi-agent coordination : a reinforcement learning approach / / Arup Kumar Sadhu, Amit Konar
Multi-agent coordination : a reinforcement learning approach / / Arup Kumar Sadhu, Amit Konar
Autore Sadhu Arup Kumar
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : IEEE Press, , 2021
Descrizione fisica 1 PDF
Disciplina 006.31
Soggetto topico Reinforcement learning
Multiagent systems
ISBN 1-119-69902-9
1-119-69899-5
1-119-69905-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PREFACE -- ACKNOWLEDGEMENT -- CHAPTER 1 INTRODUCTION: MULTI-AGENT COORDINATION BY REINFORCEMENT LEARNING AND EVOLUTIONARY ALGORITHMS 1 -- 1.1 INTRODUCTION 2 -- 1.2 SINGLE AGENT PLANNING 3 -- 1.2.1 Terminologies used in single agent planning 4 -- 1.2.2 Single agent search-based planning algorithms 9 -- 1.2.2.1 Dijkstra's algorithm 10 -- 1.2.2.2 A* (A-star) Algorithm 12 -- 1.2.2.3 D* (D-star) Algorithm 14 -- 1.2.2.4 Planning by STRIPS-like language 16 -- 1.2.3 Single agent reinforcement learning 16 -- 1.2.3.1 Multi-Armed Bandit Problem 17 -- 1.2.3.2 Dynamic programming and Bellman equation 19 -- 1.2.3.3 Correlation between reinforcement learning and Dynamic programming 20 -- 1.2.3.4 Single agent Q-learning 20 -- 1.2.3.5 Single agent planning using Q-learning 23 -- 1.3 MULTI-AGENT PLANNING AND COORDINATION 24 -- 1.3.1 Terminologies related to multi-agent coordination 24 -- 1.3.2 Classification of multi-agent system 25 -- 1.3.3 Game theory for multi-agent coordination 27 -- 1.3.3.1 Nash equilibrium (NE) 30 -- 1.3.3.1.1 Pure strategy NE (PSNE) 31 -- 1.3.3.1.2 Mixed strategy NE (MSNE) 33 -- 1.3.3.2 Correlated equilibrium (CE) 36 -- 1.3.3.3 Static game examples 37 -- 1.3.4 Correlation among RL, DP, and GT 39 -- 1.3.5 Classification of MARL 39 -- 1.3.5.1 Cooperative multi-agent reinforcement learning 41 -- 1.3.5.1.1 Static 41 -- Independent Learner (IL) and Joint Action Learner (JAL) 41Frequency maximum Q-value (FMQ) heuristic 44 -- 1.3.5.1.2 Dynamic 46 -- Team-Q 46 -- Distributed -Q 47 -- Optimal Adaptive Learning 50 -- Sparse cooperative Q-learning (SCQL) 52 -- Sequential Q-learning (SQL) 53 -- Frequency of the maximum reward Q-learning (FMRQ) 53 -- 1.3.5.2 Competitive multi-agent reinforcement learning 55 -- 1.3.5.2.1 Minimax-Q Learning 55 -- 1.3.5.2.2 Heuristically-accelerated multi-agent reinforcement learning 56 -- 1.3.5.3 Mixed multi-agent reinforcement learning 57 -- 1.3.5.3.1 Static 57 -- Belief-based Learning rule 57 -- Fictitious play 57 -- Meta strategy 58 -- Adapt When Everybody is Stationary, Otherwise Move to Equilibrium (AWESOME) 60.
Hyper-Q 62 -- Direct policy search based 63 -- Fixed learning rate 63 -- Infinitesimal Gradient Ascent (IGA) 63 -- Generalized Infinitesimal Gradient Ascent (GIGA) 65 -- Variable learning rate 66 -- Win or Learn Fast-IGA (WoLF-IGA) 66 -- GIGA-Win or Learn Fast (GIGA-WoLF) 66 -- 1.3.5.3.2 Dynamic 67 -- Equilibrium dependent 67 -- Nash-Q Learning 67 -- Correlated-Q Learning (CQL) 68 -- Asymmetric-Q Learning (AQL) 68 -- Friend-or-Foe Q-learning 70 -- Negotiation-based Q-learning 71 -- MAQL with equilibrium transfer 74 -- Equilibrium independent 76 -- Variable learning rate 76 -- Win or Learn Fast Policy hill-climbing (WoLF-PHC) 76 -- Policy Dynamic based Win or Learn Fast (PD-WoLF) 78 -- Fixed learning rate 78 -- Non-Stationary Converging Policies (NSCP) 78 -- Extended Optimal Response Learning (EXORL) 79 -- 1.3.6 Coordination and planning by MAQL 80 -- 1.3.7 Performance analysis of MAQL and MAQL-based coordination 81 -- 1.4 COORDINATION BY OPTIMIZATION ALGORITHM 83 -- 1.4.1 Particle Swarm Optimization (PSO) Algorithm 84 -- 1.4.2 Firefly Algorithm (FA) 87 -- 1.4.2.1 Initialization 87 -- 1.4.2.2 Attraction to Brighter Fireflies 87 -- 1.4.2.3 Movement of Fireflies 88 -- 1.4.3 Imperialist Competitive Algorithm (ICA) 89 -- 1.4.3.1 Initialization 89 -- 1.4.3.2 Selection of Imperialists and Colonies 89 -- 1.4.3.3 Formation of Empires 89 -- 1.4.3.4 Assimilation of Colonies 90 -- 1.4.3.5 Revolution 91 -- 1.4.3.6 Imperialistic Competition 91 -- 1.4.3.6.1 Total Empire Power Evaluation 91 -- 1.4.3.6.2 Reassignment of Colonies and Removal of Empire 92 -- 1.4.3.6.3 Union of Empires 92 -- 1.4.4 Differential evolutionary (DE) algorithm 93 -- 1.4.4.1 Initialization 93 -- 1.4.4.2 Mutation 93 -- 1.4.4.3 Recombination 93 -- 1.4.4.4 Selection 93 -- 1.4.5 Offline optimization 94 -- 1.4.6 Performance analysis of optimization algorithms 94 -- 1.4.6.1 Friedman test 94 -- 1.4.6.2 Iman-Davenport test 95 -- 1.5 SCOPE OF THE Book 95 -- 1.6 SUMMARY 98 -- References 98 -- CHAPTER 2 IMPROVE CONVERGENCE SPEED OF MULTI-AGENT Q-LEARNING FOR COOPERATIVE TASK-PLANNING 107.
2.1 INTRODUCTION 108 -- 2.2 LITERATURE REVIEW 112 -- 2.3 PRELIMINARIES 114 -- 2.3.1 Single agent Q-learning 114 -- 2.3.2 Multi-agent Q-learning 115 -- 2.4 PROPOSED MULTI-AGENT Q-LEARNING 118 -- 2.4.1 Two useful properties 119 -- 2.5 PROPOSED FCMQL ALGORITHMS AND THEIR CONVERGENCE ANALYSIS 120 -- 2.5.1 Proposed FCMQL algorithms 120 -- 2.5.2 Convergence analysis of the proposed FCMQL algorithms 121 -- 2.6 FCMQL-BASED COOPERATIVE MULTI-AGENT PLANNING 122 -- 2.7 EXPERIMENTS AND RESULTS 123 -- 2.8 CONCLUSIONS 130 -- 2.9 SUMMARY 131 -- 2.10 APPENDIX 2.1 131 -- 2.11 APPENDIX 2.2 135 -- References 152 -- CHAPTER 3 CONSENSUS Q-LEARNING FOR MULTI-AGENT COOPERATIVE PLANNING 157 -- 3.1 INTRODUCTION 158 -- 3.2 PRELIMINARIES 159 -- 3.2.1 Single agent Q-learning 159 -- 3.2.2 Equilibrium-based multi-agent Q-learning 160 -- 3.3 CONSENSUS 161 -- 3.4 PROPOSED CONSENSUS Q-LEARNING AND PLANNING 162 -- 3.4.1 Consensus Q-learning 162 -- 3.4.2 Consensus-based multi-robot planning 164 -- 3.5 EXPERIMENTS AND RESULTS 165 -- 3.5.1 Experimental setup 165 -- 3.5.2 Experiments for CoQL 165 -- 3.5.3 Experiments for consensus-based planning 166 -- 3.6 CONCLUSIONS 168 -- 3.7 SUMMARY 168 -- References 168 -- CHAPTER 4 AN EFFICIENT COMPUTING OF CORRELATED EQUILIBRIUM FOR COOPERATIVE Q-LEARNING BASED MULTI-AGENT PLANNING 171 -- 4.1 INTRODUCTION 172 -- 4.2 SINGLE-AGENT Q-LEARNING AND EQUILIBRIUM BASED MAQL 175 -- 4.2.1 Single Agent Q learning 175 -- 4.2.2 Equilibrium based MAQL 175 -- 4.3 PROPOSED COOPERATIVE MULTI-AGENT Q-LEARNING AND PLANNING 176 -- 4.3.1 Proposed schemes with their applicability 176 -- 4.3.2 Immediate rewards in Scheme-I and -II 177 -- 4.3.3 Scheme-I induced MAQL 178 -- 4.3.4 Scheme-II induced MAQL 180 -- 4.3.5 Algorithms for scheme-I and II 182 -- 4.3.6 Constraint QL-I/ QL-II(C ......................................................... 183 -- 4.3.7 Convergence 183 -- Multi-agent planning 185 -- 4.4 COMPLEXITY ANALYSIS 186 -- 4.4.1 Complexity of Correlated Q-Learning 187 -- 4.4.1.1 Space Complexity 187.
4.4.1.2 Time Complexity 187 -- 4.4.2 Complexity of the proposed algorithms 188 -- 4.4.2.1 Space Complexity 188 -- 4.4.2.2 Time Complexity 188 -- 4.4.3 Complexity comparison 189 -- 4.4.3.1 Space complexity 190 -- 4.4.3.2 Time complexity 190 -- 4.5 SIMULATION AND EXPERIMENTAL RESULTS 191 -- 4.5.1 Experimental platform 191 -- 4.5.1.1 Simulation 191 -- 4.5.1.2 Hardware 192 -- 4.5.2 Experimental approach 192 -- 4.5.2.1 Learning phase 193 -- 4.5.2.2 Planning phase 193 -- 4.5.3 Experimental results 194 -- 4.6 CONCLUSION 201 -- 4.7 SUMMARY 202 -- 4.8 APPENDIX 203 -- References 209 -- CHAPTER 5 A MODIFIED IMPERIALIST COMPETITIVE ALGORITHM FOR MULTI-AGENT STICK- CARRYING APPLICATION 213 -- 5.1 INTRODUCTION 214 -- 5.2 PROBLEM FORMULATION FOR MULTI-ROBOT STICK-CARRYING 219 -- 5.3 PROPOSED HYBRID ALGORITHM 222 -- 5.3.1 An Overview of Imperialist Competitive Algorithm (ICA) 222 -- 5.3.1.1 Initialization 222 -- 5.3.1.2 Selection of Imperialists and Colonies 223 -- 5.3.1.3 Formation of Empires 223 -- 5.3.1.4 Assimilation of Colonies 223 -- 5.3.1.5 Revolution 224 -- 5.3.1.6 Imperialistic Competition 224 -- 5.3.1.6.1 Total Empire Power Evaluation 225 -- 5.3.1.6.2 Reassignment of Colonies and Removal of Empire 225 -- 5.3.1.6.3 Union of Empires 226 -- 5.4 AN OVERVIEW OF FIREFLY ALGORITHM (FA) 226 -- 5.4.1 Initialization 226 -- 5.4.2 Attraction to Brighter Fireflies 226 -- 5.4.3 Movement of Fireflies 227 -- 5.5 PROPOSED IMPERIALIST COMPETITIVE FIREFLY ALGORITHM 227 -- 5.5.1 Assimilation of Colonies 229 -- 5.5.1.1 Attraction to Powerful Colonies 230 -- 5.5.1.2 Modification of Empire Behavior 230 -- 5.5.1.3 Union of Empires 230 -- 5.6 SIMULATION RESULTS 232 -- 5.6.1 Comparative Framework 232 -- 5.6.2 Parameter Settings 232 -- 5.6.3 Analysis on Explorative Power of ICFA 232 -- 5.6.4 Comparison of Quality of the Final Solution 233 -- 5.6.5 Performance Analysis 233 -- 5.7 COMPUTER SIMULATION AND EXPERIMENT 240 -- 5.7.1 Average total path deviation (ATPD) 240 -- 5.7.2 Average Uncovered Target Distance (AUTD) 241.
5.7.3 Experimental Setup in Simulation Environment 241 -- 5.7.4 Experimental Results in Simulation Environment 242 -- 5.7.5 Experimental Setup with Khepera Robots 244 -- 5.7.6 Experimental Results with Khepera Robots 244 -- 5.8 CONCLUSION 245 -- 5.9 SUMMARY 247 -- 5.10 APPENDIX 5.1 248 -- References 249 -- CHAPTER 6 CONCLUSIONS AND FUTURE DIRECTIONS 255 -- 6.1 CONCLUSIONS 256 -- 6.2 FUTURE DIRECTIONS 257.
Record Nr. UNINA-9910554865903321
Sadhu Arup Kumar  
Hoboken, NJ : , : Wiley : , : IEEE Press, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multi-agent coordination : a reinforcement learning approach / / Arup Kumar Sadhu, Amit Konar
Multi-agent coordination : a reinforcement learning approach / / Arup Kumar Sadhu, Amit Konar
Autore Sadhu Arup Kumar
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : IEEE Press, , 2021
Descrizione fisica 1 PDF
Disciplina 006.31
Soggetto topico Reinforcement learning
Multiagent systems
ISBN 1-119-69902-9
1-119-69899-5
1-119-69905-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PREFACE -- ACKNOWLEDGEMENT -- CHAPTER 1 INTRODUCTION: MULTI-AGENT COORDINATION BY REINFORCEMENT LEARNING AND EVOLUTIONARY ALGORITHMS 1 -- 1.1 INTRODUCTION 2 -- 1.2 SINGLE AGENT PLANNING 3 -- 1.2.1 Terminologies used in single agent planning 4 -- 1.2.2 Single agent search-based planning algorithms 9 -- 1.2.2.1 Dijkstra's algorithm 10 -- 1.2.2.2 A* (A-star) Algorithm 12 -- 1.2.2.3 D* (D-star) Algorithm 14 -- 1.2.2.4 Planning by STRIPS-like language 16 -- 1.2.3 Single agent reinforcement learning 16 -- 1.2.3.1 Multi-Armed Bandit Problem 17 -- 1.2.3.2 Dynamic programming and Bellman equation 19 -- 1.2.3.3 Correlation between reinforcement learning and Dynamic programming 20 -- 1.2.3.4 Single agent Q-learning 20 -- 1.2.3.5 Single agent planning using Q-learning 23 -- 1.3 MULTI-AGENT PLANNING AND COORDINATION 24 -- 1.3.1 Terminologies related to multi-agent coordination 24 -- 1.3.2 Classification of multi-agent system 25 -- 1.3.3 Game theory for multi-agent coordination 27 -- 1.3.3.1 Nash equilibrium (NE) 30 -- 1.3.3.1.1 Pure strategy NE (PSNE) 31 -- 1.3.3.1.2 Mixed strategy NE (MSNE) 33 -- 1.3.3.2 Correlated equilibrium (CE) 36 -- 1.3.3.3 Static game examples 37 -- 1.3.4 Correlation among RL, DP, and GT 39 -- 1.3.5 Classification of MARL 39 -- 1.3.5.1 Cooperative multi-agent reinforcement learning 41 -- 1.3.5.1.1 Static 41 -- Independent Learner (IL) and Joint Action Learner (JAL) 41Frequency maximum Q-value (FMQ) heuristic 44 -- 1.3.5.1.2 Dynamic 46 -- Team-Q 46 -- Distributed -Q 47 -- Optimal Adaptive Learning 50 -- Sparse cooperative Q-learning (SCQL) 52 -- Sequential Q-learning (SQL) 53 -- Frequency of the maximum reward Q-learning (FMRQ) 53 -- 1.3.5.2 Competitive multi-agent reinforcement learning 55 -- 1.3.5.2.1 Minimax-Q Learning 55 -- 1.3.5.2.2 Heuristically-accelerated multi-agent reinforcement learning 56 -- 1.3.5.3 Mixed multi-agent reinforcement learning 57 -- 1.3.5.3.1 Static 57 -- Belief-based Learning rule 57 -- Fictitious play 57 -- Meta strategy 58 -- Adapt When Everybody is Stationary, Otherwise Move to Equilibrium (AWESOME) 60.
Hyper-Q 62 -- Direct policy search based 63 -- Fixed learning rate 63 -- Infinitesimal Gradient Ascent (IGA) 63 -- Generalized Infinitesimal Gradient Ascent (GIGA) 65 -- Variable learning rate 66 -- Win or Learn Fast-IGA (WoLF-IGA) 66 -- GIGA-Win or Learn Fast (GIGA-WoLF) 66 -- 1.3.5.3.2 Dynamic 67 -- Equilibrium dependent 67 -- Nash-Q Learning 67 -- Correlated-Q Learning (CQL) 68 -- Asymmetric-Q Learning (AQL) 68 -- Friend-or-Foe Q-learning 70 -- Negotiation-based Q-learning 71 -- MAQL with equilibrium transfer 74 -- Equilibrium independent 76 -- Variable learning rate 76 -- Win or Learn Fast Policy hill-climbing (WoLF-PHC) 76 -- Policy Dynamic based Win or Learn Fast (PD-WoLF) 78 -- Fixed learning rate 78 -- Non-Stationary Converging Policies (NSCP) 78 -- Extended Optimal Response Learning (EXORL) 79 -- 1.3.6 Coordination and planning by MAQL 80 -- 1.3.7 Performance analysis of MAQL and MAQL-based coordination 81 -- 1.4 COORDINATION BY OPTIMIZATION ALGORITHM 83 -- 1.4.1 Particle Swarm Optimization (PSO) Algorithm 84 -- 1.4.2 Firefly Algorithm (FA) 87 -- 1.4.2.1 Initialization 87 -- 1.4.2.2 Attraction to Brighter Fireflies 87 -- 1.4.2.3 Movement of Fireflies 88 -- 1.4.3 Imperialist Competitive Algorithm (ICA) 89 -- 1.4.3.1 Initialization 89 -- 1.4.3.2 Selection of Imperialists and Colonies 89 -- 1.4.3.3 Formation of Empires 89 -- 1.4.3.4 Assimilation of Colonies 90 -- 1.4.3.5 Revolution 91 -- 1.4.3.6 Imperialistic Competition 91 -- 1.4.3.6.1 Total Empire Power Evaluation 91 -- 1.4.3.6.2 Reassignment of Colonies and Removal of Empire 92 -- 1.4.3.6.3 Union of Empires 92 -- 1.4.4 Differential evolutionary (DE) algorithm 93 -- 1.4.4.1 Initialization 93 -- 1.4.4.2 Mutation 93 -- 1.4.4.3 Recombination 93 -- 1.4.4.4 Selection 93 -- 1.4.5 Offline optimization 94 -- 1.4.6 Performance analysis of optimization algorithms 94 -- 1.4.6.1 Friedman test 94 -- 1.4.6.2 Iman-Davenport test 95 -- 1.5 SCOPE OF THE Book 95 -- 1.6 SUMMARY 98 -- References 98 -- CHAPTER 2 IMPROVE CONVERGENCE SPEED OF MULTI-AGENT Q-LEARNING FOR COOPERATIVE TASK-PLANNING 107.
2.1 INTRODUCTION 108 -- 2.2 LITERATURE REVIEW 112 -- 2.3 PRELIMINARIES 114 -- 2.3.1 Single agent Q-learning 114 -- 2.3.2 Multi-agent Q-learning 115 -- 2.4 PROPOSED MULTI-AGENT Q-LEARNING 118 -- 2.4.1 Two useful properties 119 -- 2.5 PROPOSED FCMQL ALGORITHMS AND THEIR CONVERGENCE ANALYSIS 120 -- 2.5.1 Proposed FCMQL algorithms 120 -- 2.5.2 Convergence analysis of the proposed FCMQL algorithms 121 -- 2.6 FCMQL-BASED COOPERATIVE MULTI-AGENT PLANNING 122 -- 2.7 EXPERIMENTS AND RESULTS 123 -- 2.8 CONCLUSIONS 130 -- 2.9 SUMMARY 131 -- 2.10 APPENDIX 2.1 131 -- 2.11 APPENDIX 2.2 135 -- References 152 -- CHAPTER 3 CONSENSUS Q-LEARNING FOR MULTI-AGENT COOPERATIVE PLANNING 157 -- 3.1 INTRODUCTION 158 -- 3.2 PRELIMINARIES 159 -- 3.2.1 Single agent Q-learning 159 -- 3.2.2 Equilibrium-based multi-agent Q-learning 160 -- 3.3 CONSENSUS 161 -- 3.4 PROPOSED CONSENSUS Q-LEARNING AND PLANNING 162 -- 3.4.1 Consensus Q-learning 162 -- 3.4.2 Consensus-based multi-robot planning 164 -- 3.5 EXPERIMENTS AND RESULTS 165 -- 3.5.1 Experimental setup 165 -- 3.5.2 Experiments for CoQL 165 -- 3.5.3 Experiments for consensus-based planning 166 -- 3.6 CONCLUSIONS 168 -- 3.7 SUMMARY 168 -- References 168 -- CHAPTER 4 AN EFFICIENT COMPUTING OF CORRELATED EQUILIBRIUM FOR COOPERATIVE Q-LEARNING BASED MULTI-AGENT PLANNING 171 -- 4.1 INTRODUCTION 172 -- 4.2 SINGLE-AGENT Q-LEARNING AND EQUILIBRIUM BASED MAQL 175 -- 4.2.1 Single Agent Q learning 175 -- 4.2.2 Equilibrium based MAQL 175 -- 4.3 PROPOSED COOPERATIVE MULTI-AGENT Q-LEARNING AND PLANNING 176 -- 4.3.1 Proposed schemes with their applicability 176 -- 4.3.2 Immediate rewards in Scheme-I and -II 177 -- 4.3.3 Scheme-I induced MAQL 178 -- 4.3.4 Scheme-II induced MAQL 180 -- 4.3.5 Algorithms for scheme-I and II 182 -- 4.3.6 Constraint QL-I/ QL-II(C ......................................................... 183 -- 4.3.7 Convergence 183 -- Multi-agent planning 185 -- 4.4 COMPLEXITY ANALYSIS 186 -- 4.4.1 Complexity of Correlated Q-Learning 187 -- 4.4.1.1 Space Complexity 187.
4.4.1.2 Time Complexity 187 -- 4.4.2 Complexity of the proposed algorithms 188 -- 4.4.2.1 Space Complexity 188 -- 4.4.2.2 Time Complexity 188 -- 4.4.3 Complexity comparison 189 -- 4.4.3.1 Space complexity 190 -- 4.4.3.2 Time complexity 190 -- 4.5 SIMULATION AND EXPERIMENTAL RESULTS 191 -- 4.5.1 Experimental platform 191 -- 4.5.1.1 Simulation 191 -- 4.5.1.2 Hardware 192 -- 4.5.2 Experimental approach 192 -- 4.5.2.1 Learning phase 193 -- 4.5.2.2 Planning phase 193 -- 4.5.3 Experimental results 194 -- 4.6 CONCLUSION 201 -- 4.7 SUMMARY 202 -- 4.8 APPENDIX 203 -- References 209 -- CHAPTER 5 A MODIFIED IMPERIALIST COMPETITIVE ALGORITHM FOR MULTI-AGENT STICK- CARRYING APPLICATION 213 -- 5.1 INTRODUCTION 214 -- 5.2 PROBLEM FORMULATION FOR MULTI-ROBOT STICK-CARRYING 219 -- 5.3 PROPOSED HYBRID ALGORITHM 222 -- 5.3.1 An Overview of Imperialist Competitive Algorithm (ICA) 222 -- 5.3.1.1 Initialization 222 -- 5.3.1.2 Selection of Imperialists and Colonies 223 -- 5.3.1.3 Formation of Empires 223 -- 5.3.1.4 Assimilation of Colonies 223 -- 5.3.1.5 Revolution 224 -- 5.3.1.6 Imperialistic Competition 224 -- 5.3.1.6.1 Total Empire Power Evaluation 225 -- 5.3.1.6.2 Reassignment of Colonies and Removal of Empire 225 -- 5.3.1.6.3 Union of Empires 226 -- 5.4 AN OVERVIEW OF FIREFLY ALGORITHM (FA) 226 -- 5.4.1 Initialization 226 -- 5.4.2 Attraction to Brighter Fireflies 226 -- 5.4.3 Movement of Fireflies 227 -- 5.5 PROPOSED IMPERIALIST COMPETITIVE FIREFLY ALGORITHM 227 -- 5.5.1 Assimilation of Colonies 229 -- 5.5.1.1 Attraction to Powerful Colonies 230 -- 5.5.1.2 Modification of Empire Behavior 230 -- 5.5.1.3 Union of Empires 230 -- 5.6 SIMULATION RESULTS 232 -- 5.6.1 Comparative Framework 232 -- 5.6.2 Parameter Settings 232 -- 5.6.3 Analysis on Explorative Power of ICFA 232 -- 5.6.4 Comparison of Quality of the Final Solution 233 -- 5.6.5 Performance Analysis 233 -- 5.7 COMPUTER SIMULATION AND EXPERIMENT 240 -- 5.7.1 Average total path deviation (ATPD) 240 -- 5.7.2 Average Uncovered Target Distance (AUTD) 241.
5.7.3 Experimental Setup in Simulation Environment 241 -- 5.7.4 Experimental Results in Simulation Environment 242 -- 5.7.5 Experimental Setup with Khepera Robots 244 -- 5.7.6 Experimental Results with Khepera Robots 244 -- 5.8 CONCLUSION 245 -- 5.9 SUMMARY 247 -- 5.10 APPENDIX 5.1 248 -- References 249 -- CHAPTER 6 CONCLUSIONS AND FUTURE DIRECTIONS 255 -- 6.1 CONCLUSIONS 256 -- 6.2 FUTURE DIRECTIONS 257.
Record Nr. UNINA-9910812040703321
Sadhu Arup Kumar  
Hoboken, NJ : , : Wiley : , : IEEE Press, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui