Foreword	7
	Preface	9
	Acknowledgments	17
	Contents	18
	About the Authors	25
	Part I: Fundamental Concepts of Optimization Problems and Theory of Meta-Heuristic Music-Inspired Optimization Algorithms	27
	Chapter 1: Introduction to Meta-heuristic Optimization Algorithms	28
	1.1 Introduction	28
	1.2 An Optimization Problem and Its Parameters	29
	1.2.1 Mathematical Description of an Optimization Problem	29
	1.3 Classification of an Optimization Problem	31
	1.3.1 Classification of Optimization Problems from the Perspective of a Number of Objective Functions	31
	1.3.2 Classification of Optimization Problems from the Perspective of Constraints	32
	1.3.3 Classification of Optimization Problems from the Perspective of the Nature of Employed Equations	33
	1.3.4 Classification of Optimization Problems from the Perspective of an Objective Functions Landscape	34
	1.3.5 Classification of Optimization Problems from the Perspective of the Kind of Decision-Making Variables	34
	1.3.6 Classification of Optimization Problems from the Perspective of the Number of Decision-Making Variables	35
	1.3.7 Classification of Optimization Problems from the Perspective of the Separability of the Employed Equations	36
	1.3.8 Classification of Optimization Problems from the Perspective of Uncertainty	36
	1.4 Optimization Algorithms and Their Characteristics	37
	1.5 Meta-heuristic Optimization Algorithms	38
	1.5.1 Classification of Meta-heuristic Optimization Algorithms with a Focus on Inspirational Sources	39
	1.5.1.1 Swarm Intelligence-Based Meta-heuristic Optimization Algorithms	39
	1.5.1.2 Biologically Inspired Meta-heuristic Optimization Algorithms Not Based on Swarm Intelligence	40
	1.5.1.3 Physics- and Chemistry-Based Meta-heuristic Optimization Algorithms	40
	1.5.1.4 Human Behavior- and Society-Inspired Meta-heuristic Optimization Algorithms	41
	1.5.1.5 Some Hints Concerning the Architecture of Meta-heuristic Optimization Algorithms	41
	1.6 Conclusions	42
	Appendix 1: List of Abbreviations and Acronyms	42
	Appendix 2: List of Mathematical Symbols	43
	References	44
	Chapter 2: Introduction to Multi-objective Optimization and Decision-Making Analysis	46
	2.1 Introduction	46
	2.2 Necessity of Using Multi-objective Optimization	48
	2.3 Fundamental Concepts of Optimization in the MOOPs	49
	2.3.1 Mathematical Description of a MOOP	49
	2.3.2 Concepts Associated with Efficiency, Efficient frontier, and Dominance	50
	2.3.3 Concepts Pertaining to Pareto Optimality	51
	2.3.4 Concepts Related to the Vector of Ideal Objective Functions and the Vector of Nadir Objective Functions	53
	2.3.5 Concepts Relevant to the Investigation of Pareto Optimality	55
	2.4 Multi-objective Optimization Algorithms	55
	2.4.1 Noninteractive Approaches	56
	2.4.1.1 Basic Approaches	56
	2.4.1.2 No-Preference Approaches	60
	2.4.1.3 A Priori Approaches	60
	2.4.1.4 A Posteriori Approaches	61
	2.4.2 Interactive Approaches	61
	2.5 Selection of the Final Solution by Using a Fuzzy Satisfying Method	63
	2.5.1 Conservative Methodology	65
	2.5.2 Distance Metric Methodology	66
	2.5.3 Step-by-Step Process for Implementing the FSM	66
	2.6 Conclusions	67
	Appendix 1: List of Abbreviations and Acronyms	68
	Appendix 2: List of Mathematical Symbols	68
	References	70
	Chapter 3: Music-Inspired Optimization Algorithms: From Past to Present	71
	3.1 Introduction	71
	3.2 A Brief Review of Music	74
	3.2.1 The Definition of Music	74
	3.2.2 A Brief Review of Music History	75
	3.2.3 The Interdependencies of Phenomena and Concepts of Music and the Optimization Problem	75
	3.3 Harmony Search Algorithm	77
	3.3.1 Stage 1: Definition Stage-Definition of the Optimization Problem and its Parameters	78
	3.3.2 Stage 2: Initialization Stage	79
	3.3.2.1 Sub-stage 2.1: Initialization of the Parameters of the SS-HSA	79
	3.3.2.2 Sub-stage 2.2: Initialization of the HM	80
	3.3.3 Stage 3: Computational Stage	81
	3.3.3.1 Sub-stage 3.1: Improvisation of a New Harmony Vector	83
	3.3.3.2 Sub-stage 3.2: Update of the HM	85
	3.3.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SS-HSA	87
	3.3.4 Stage 4: Selection Stage-Selection of the Final Optimal Solution-The Best Harmony	87
	3.4 Enhanced Versions of the Single-Stage Computational, Single-Dimensional Harmony Search Algorithm	89
	3.5 Improved Harmony Search Algorithm	90
	3.6 Melody Search Algorithm	93
	3.6.1 Stage 1: Definition Stage-Definition of the Optimization Problem and its Parameters	97
	3.6.2 Stage 2: Initialization Stage	98
	3.6.2.1 Sub-stage 2.1: Initialization of the Parameters of the TMS-MSA	98
	3.6.2.2 Sub-stage 2.2: Initialization of the MM	100
	3.6.3 Stage 3: Single Computational Stage or SIS	103
	3.6.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player	103
	3.6.3.2 Sub-stage 3.2: Update of Each PM	104
	3.6.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS	105
	3.6.4 Stage 4: Pseudo-Group Computational Stage or PGIS	106
	3.6.4.1 Sub-stage 4.1: Improvisation of a New Melody Vector by Each Player Taking into Account the Feasible Ranges of the Upda...	106
	3.6.4.2 Sub-stage 4.2: Update of Each PM	106
	3.6.4.3 Sub-stage 4.3: Update of the Feasible Ranges of Pitches-Continuous Decision-Making Variables-for the Next Improvisatio...	106
	3.6.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the PGIS	107
	3.6.5 Stage 5: Selection Stage-Selection of the Final Optimal Solution-The Best Melody	108
	3.6.6 Alternative Improvisation Procedure	109
	3.7 Conclusions	115
	Appendix 1: List of Abbreviations and Acronyms	115
	Appendix 2: List of Mathematical Symbols	116
	References	119
	Chapter 4: Advances in Music-Inspired Optimization Algorithms	120
	4.1 Introduction	120
	4.2 Continuous/Discrete TMS-MSA	123
	4.2.1 Stage 1: Definition Stage-Definition of the Optimization Problem and Its Parameters	124
	4.2.2 Stage 2: Initialization Stage	125
	4.2.2.1 Sub-stage 2.1: Initialization of the Parameters of the Proposed Continuous/Discrete TMS-MSA	125
	4.2.2.2 Sub-stage 2.2: Initialization of the MM	125
	4.2.3 Stage 3: Single Computational Stage or SIS	127
	4.2.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player	127
	4.2.3.2 Sub-stage 3.2: Update of Each PM	129
	4.2.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS	130
	4.2.4 Stage 4: Pseudo-Group Computational Stage or PGIS	130
	4.2.4.1 Sub-stage 4.1: Improvisation of a New Melody Vector by Each Player	131
	4.2.4.2 Sub-stage 4.2: Update of Memory of Each Player	132
	4.2.4.3 Sub-stage 4.3: Update of the Feasible Ranges of Pitches-Continuous Decision-Making Variables for the Next Improvisatio...	132
	4.2.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the Pseudo-Group Improvisation Stage	132
	4.2.5 Stage 5: Selection Stage-Selection of the Final Optimal Solution-The Most Favorable Melody	132
	4.2.6 Continuous/Discrete Alternative Improvisation Procedure	134
	4.3 Enhanced Version of the Proposed Continuous/Discrete TMS-MSA	139
	4.4 Multi-stage Computational Multi-dimensional Multiple-Homogeneous Enhanced Melody Search Algorithm: Symphony Orchestra Sear...	157
	4.4.1 Stage 1: Definition Stage-Definition of the Optimization Problem and Its Parameters	165
	4.4.2 Stage 2: Initialization Stage	166
	4.4.2.1 Sub-stage 2.1: Initialization of the Parameters of the SOSA	166
	4.4.2.2 Sub-stage 2.2: Initialization of the Symphony Orchestra Memory	169
	4.4.3 Stage 3: Single Computational Stage or SIS	171
	4.4.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player in the Symphony Orchestra	171
	4.4.3.2 Sub-stage 3.2: Update of Each Available PM in the Symphony Orchestra	173
	4.4.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS	174
	4.4.4 Stage 4: Group Computational Stage for Each Homogeneous Musical Group or GISHMG	174
	4.4.4.1 Sub-stage 4.1: Improvisation of a New Melody Vector by Each Player in the Symphony Orchestra Taking into Account the F...	175
	4.4.4.2 Sub-stage 4.2: Update of Each Available PM in the Symphony Orchestra	177
	4.4.4.3 Sub-stage 4.3: Update of the Feasible Ranges of the Pitches-Continuous Decision-Making Variables-for Each Homogeneous ...	177
	4.4.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the GISHMG	177
	4.4.5 Stage 5: Group Computational Stage for the Inhomogeneous Musical Ensemble or GISIME	178
	4.4.5.1 Sub-stage 5.1: Improvisation of a New Melody Vector by Each Player in the Symphony Orchestra Taking into Account the F...	179
	4.4.5.2 Sub-stage 5.2: Update of Each Available PM in the Symphony Orchestra	180
	4.4.5.3 Sub-stage 5.3: Update of the Feasible Ranges of the Pitches-Continuous Decision-Making Variables-for the Inhomogeneous...	180
	4.4.5.4 Sub-stage 5.4: Check of the Stopping Criterion of the GISIME	180
	4.4.6 Stage 6: Selection Stage-Selection of the Final Optimal Solution-the Best Melody	182
	4.4.7 Novel Improvisation Procedure	183
	4.4.8 Some Hints Regarding the Architecture of the Proposed SOSA	190
	4.5 Multi-objective Strategies for the Music-Inspired Optimization Algorithms	196
	4.5.1 Multi-objective Strategies for the Meta-heuristic Music-Inspired Optimization Algorithms with Single-Stage Computational...	196
	4.5.1.1 Multi-objective Strategy for the SS-HSA	197
	4.5.1.2 Multi-objective Strategy for the SS-IHSA	212
	4.5.2 Multi-objective Strategies for the Meta-heuristic Music-Inspired Optimization Algorithms with Two-Stage Computational Mu...	215
	4.5.2.1 Multi-objective Strategy for the Proposed Continuous/Discrete TMS-MSA	215
	4.5.2.2 Multi-objective Strategy for the Proposed TMS-EMSA	235
	4.5.3 Multi-objective Strategy for the Meta-heuristic Music-Inspired Optimization Algorithms with Multi-stage Computational Mu...	245
	4.6 Conclusions	271
	Appendix 1: List of Abbreviations and Acronyms	276
	Appendix 2: List of Mathematical Symbols	278
	References	285
	Part II: Power Systems Operation and Planning Problems	286
	Chapter 5: Power Systems Operation	287
	5.1 Introduction	287
	5.2 A Brief Review of Game Theory	289
	5.2.1 Classifications of the Game	289
	5.2.2 The Concept of Nash Equilibrium	291
	5.2.3 Modeling of Game Theory in the Electricity Markets with Imperfect Competition	292
	5.2.3.1 Cournot-Based Model and/or Playing with Quantities	292
	5.2.3.2 Stackelberg Leadership-Based Model	294
	5.2.3.3 Bertrand-Based Model and Playing with Prices	294
	5.2.3.4 The Supply Function Equilibrium-Based Model	295
	5.3 A Bilateral Bidding Mechanism in the Competitive Security-Constrained Electricity Market: A Bi-Level Computational-Logical...	298
	5.3.1 Bilateral Bidding Strategy Model: First Level (Problem A)	299
	5.3.1.1 Mathematical Model of Bidding Strategies for GENCOs	302
	5.3.1.2 Mathematical Model of a Bidding Strategy for DISCOs	305
	5.3.2 Security-Constrained Electricity Market Model: Second Level (Problem B)	308
	5.3.3 Overview of the Bi-Level Computational-Logical Framework	312
	5.3.4 Solution Method and Implementation Considerations	314
	5.3.5 Simulation Results and Case Studies	315
	5.3.5.1 First Case: Simulation Results and Discussion	318
	5.3.5.2 Second Case: Simulation Results and Discussion	321
	5.3.5.3 Performance Evaluation of the Proposed Music-Inspired Optimization Algorithms	331
	5.4 Conclusions	335
	Appendix 1: List of Abbreviations and Acronyms	337
	Appendix 2: List of Mathematical Symbols	338
	Appendix 3: Input data	340
	References	346
	Chapter 6: Power Systems Planning	348
	6.1 Introduction	348
	6.2 A Brief Review of Power System Planning Studies	350
	6.2.1 Why Do the Power Systems Need the Expansion Planning?	350
	6.2.2 A Brief Review of Power System Planning Structure	350
	6.2.3 Power System Planning Issues	351
	6.2.3.1 From the Standpoint of Power System Structure	352
	6.2.3.2 From the Standpoint of the Planning Horizon	353
	6.2.3.3 From the Standpoint of the Uncertainties	354
	6.2.3.4 From the Standpoint of the Solving Algorithms	357
	6.3 Pseudo-Dynamic Generation Expansion Planning: A Strategic Tri-level Computational-Logical Framework	358
	6.3.1 Mathematical Model of the Deterministic Strategic Tri-level Computational-Logical Framework	359
	6.3.1.1 Bilateral Bidding Mechanism: First Level (Problem A)	362
	6.3.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B)	362
	6.3.1.3 Pseudo-Dynamic Generation Expansion Planning: Third Level (Problem C)	363
	6.3.2 Overview of the Deterministic Strategic Tri-level Computational-Logical Framework	368
	6.3.3 Mathematical Model of the Risk-Driven Strategic Tri-level Computational-Logical Framework	372
	6.3.3.1 The IGDT Severe Twofold Uncertainty Model	373
	6.3.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function	376
	6.3.3.3 The IGDT Risk-Taker Decision-Making Strategy: Opportunity Function	379
	6.3.4 Solution Method and Implementation Considerations	382
	6.3.5 Simulation Results and Case Studies	383
	6.3.5.1 First Case: Simulation Results and Discussion	387
	6.3.5.2 Second Case: Simulation Results and Discussion	399
	6.3.5.3 Quantitative Verification of the Proposed IGDT Risk-Taker Decision-Making Policy in Comparison to a Robust Optimizatio...	413
	6.3.5.4 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion	413
	6.4 Pseudo-Dynamic Transmission Expansion Planning: A Strategic Tri-level Computational-Logical Framework	423
	6.4.1 Mathematical Model of the Deterministic Strategic Tri-level Computational-Logical Framework	426
	6.4.1.1 Bilateral Bidding Mechanism: First Level (Problem A)	426
	6.4.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B)	426
	6.4.1.3 Pseudo-Dynamic Transmission Expansion Planning: Third Level (Problem C)	426
	6.4.2 Overview of the Deterministic Strategic Tri-level Computational-Logical Framework	431
	6.4.3 Mathematical Model of the Risk-Driven Strategic Tri-level Computational-Logical Framework	434
	6.4.3.1 The IGDT Severe Twofold Uncertainty Model	435
	6.4.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function	435
	6.4.3.3 The IGDT Risk-Taker Decision-Making Policy: Opportunity Function	438
	6.4.4 Solution Method and Implementation Considerations	440
	6.4.5 Simulation Results and Case Studies	441
	6.4.5.1 The Modified IEEE 30-Bus Test System	443
	6.4.5.1.1 First Case: Simulation Results and Discussion	446
	6.4.5.1.2 Second Case: Simulation Results and Discussion	456
	6.4.5.2 Large-Scale Iranian 400 kV Transmission Network	463
	6.4.5.2.1 First Case: Simulation Results and Discussion	471
	6.4.5.2.2 Second Case: Simulation Results and Discussion	471
	6.4.5.2.3 Investigation of the Effects of Volatility in Market Price and Demand Uncertainties	473
	6.4.5.2.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimi...	478
	6.4.5.2.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion	481
	6.5 Coordination of Pseudo-Dynamic Generation and Transmission Expansion Planning: A Strategic Quad-Level Computational-Logica...	487
	6.5.1 Mathematical Model of the Deterministic Strategic Quad-Level Computational-Logical Framework	488
	6.5.1.1 Bilateral Bidding Mechanism: First Level (Problem A)	488
	6.5.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B)	491
	6.5.1.3 Pseudo-Dynamic Generation Expansion Planning: Third Level (Problem C)	492
	6.5.1.4 Pseudo-Dynamic Transmission Expansion Planning: Fourth Level (Problem D)	492
	6.5.2 Overview of the Deterministic Strategic Quad-Level Computational-Logical Framework	492
	6.5.3 Mathematical Model of the Risk-Driven Strategic Quad-Level Computational-Logical Framework	500
	6.5.3.1 The IGDT Severe Twofold Uncertainty Model	500
	6.5.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function	500
	6.5.3.3 The IGDT Risk-Taker Decision-Making Policy: Opportunity Function	503
	6.5.4 Solution Method and Implementation Considerations	506
	6.5.5 Simulation Results and Case Studies	509
	6.5.5.1 First Case: Simulation Results and Discussion	514
	6.5.5.2 Second Case: Simulation Results and Discussion	517
	6.5.5.3 Investigation into the Performance of the Proposed Framework Under the Coordinated and Uncoordinated Decisions for the...	520
	6.5.5.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimiza...	524
	6.5.5.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion	527
	6.6 Pseudo-Dynamic Open-Loop Distribution Expansion Planning: A Techno-Economic Framework	540
	6.6.1 Mathematical Model of the Deterministic Techno-Economic Framework	541
	6.6.2 Mathematical Model of the Risk-Driven Techno-Economic Framework	553
	6.6.2.1 The IGDT Severe Twofold Uncertainty Model	553
	6.6.2.2 The IGDT Risk-Averse Decision-Making Model: Robustness Function	555
	6.6.2.3 The IGDT Risk-Taker Decision-Making Model: Opportunity Function	556
	6.6.3 Solution Method and Implementation Considerations	558
	6.6.4 Simulation Results and Case Studies	559
	6.6.4.1 First Case: Simulation Results and Discussion	563
	6.6.4.2 Second Case: Simulation Results and Discussion	569
	6.6.4.3 The Impact of the Presence of Distributed Generation Resources on the Voltage Profile	574
	6.6.4.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimiza...	576
	6.6.4.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion	578
	6.7 Conclusions	589
	Appendix 1: List of Abbreviations and Acronyms	592
	Appendix 2: List of Mathematical Symbols	594
	Appendix 3: Input Data	607
	References	642
	Chapter 7: Power Filters Planning	647
	7.1 Introduction	647
	7.2 A Brief Review of Harmonic Power Filter Planning Studies	649
	7.2.1 Nonlinear Loads and Their Malicious Effects	650
	7.2.2 Harmonic Power Filters	651
	7.2.3 Harmonic Power Flow	653
	7.2.4 Harmonic Power Filter Planning Problem	654
	7.3 Hybrid Harmonic Power Filter Planning: A Techno-economic Framework	655
	7.3.1 Mathematical Model of the Techno-economic Multi-objective Framework	656
	7.3.1.1 Deterministic Decoupled Harmonic Power Flow Methodology	659
	7.3.1.2 Passive and Active Harmonic Power Filters	666
	7.3.1.3 Hybrid Harmonic Power Filter Planning Problem	670
	7.3.1.4 Probabilistic Decoupled Harmonic Power Flow Methodology	677
	7.3.2 Solution Method and Implementation Considerations	681
	7.3.3 Simulation Results and Case Studies	681
	7.3.3.1 IEEE 18-Bus Distorted Test Network	682
	7.3.3.1.1 First Case: Simulation Results and Discussion	687
	7.3.3.1.2 Second Case: Simulation Results and Discussion	692
	7.3.3.1.3 Third Case: Simulation Results and Discussion	695
	7.3.3.1.4 Investigation of Passive Harmonic Power Filter Performance	700
	7.3.3.2 The 34-Bus Distribution Test Network	701
	7.3.3.2.1 First Case: Simulation Results and Discussion	704
	7.3.3.2.2 Second Case: Simulation Results and Discussion	705
	7.3.3.2.3 Third Case: Simulation Results and Discussion	706
	7.3.3.2.4 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion	709
	7.4 Conclusions	717
	Appendix 1: List of Abbreviations and Acronyms	720
	Appendix 2: List of Mathematical Symbols	721
	Appendix 3: Input Data	727
	References	735
	Index	737