Configurable Intelligent Optimization Algorithm – Design and Practice in Manufacturing

Configurable Intelligent Optimization Algorithm – Design and Practice in Manufacturing
اسم المؤلف
Fei Tao , Lin Zhang , Yuanjun Laili
التاريخ
12 أكتوبر 2023
المشاهدات
198
التقييم
(لا توجد تقييمات)
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Configurable Intelligent Optimization Algorithm – Design and Practice in Manufacturing
Fei Tao , Lin Zhang , Yuanjun Laili
Contents
Part I Introduction and Overview
1 Brief History and Overview of Intelligent Optimization
Algorithms . 3
1.1 Introduction 3
1.2 Brief History of Intelligent Optimization Algorithms . 5
1.3 Classification of Intelligent Algorithms . 8
1.4 Brief Review of Typical Intelligent
Optimization Algorithms . 12
1.4.1 Review of Evolutionary Learning Algorithms 12
1.4.2 Review of Neighborhood Search Algorithms . 16
1.4.3 Review of Swarm Intelligence Algorithm . 20
1.5 The Classification of Current Studies on Intelligent
Optimization Algorithm 23
1.5.1 Algorithm Innovation . 23
1.5.2 Algorithm Improvement . 24
1.5.3 Algorithm Hybridization . 25
1.5.4 Algorithm Parallelization . 26
1.5.5 Algorithm Application 26
1.6 Development Trends 28
1.6.1 Intellectualization 28
1.6.2 Service-Orientation . 29
1.6.3 Application-Oriented 29
1.6.4 User-Centric . 29
1.7 Summary 30
References . 31
vii2 Recent Advances of Intelligent Optimization Algorithm
in Manufacturing 35
2.1 Introduction 35
2.2 Classification of Optimization Problems in Manufacturing . 37
2.2.1 Numerical Function Optimization . 38
2.2.2 Parameter Optimization 38
2.2.3 Detection and Classification . 39
2.2.4 Combinatorial Scheduling 40
2.2.5 Multi-disciplinary Optimization 41
2.2.6 Summary of the Five Types of Optimization
Problems in Manufacturing . 42
2.3 Challenges for Addressing Optimization Problems
in Manufacturing 44
2.3.1 Balance of Multi-objectives . 44
2.3.2 Handling of Multi-constraints 46
2.3.3 Extraction of Priori Knowledge 47
2.3.4 Modeling of Uncertainty and Dynamics 48
2.3.5 Transformation of Qualitative and Quantitative
Features . 50
2.3.6 Simplification of Large-Scale Solution Space . 51
2.3.7 Jumping Out of Local Convergence . 52
2.4 An Overview of Optimization Methods in Manufacturing 52
2.4.1 Empirical-Based Method . 53
2.4.2 Prediction-Based Method . 54
2.4.3 Simulation-Based Method 55
2.4.4 Model-Based Method . 55
2.4.5 Tool-Based Method 56
2.4.6 Advanced-Computing-Technology-Based Method 56
2.4.7 Summary of Studies on Solving Methods . 57
2.5 Intelligent Optimization Algorithms for Optimization
Problems in Manufacturing . 58
2.6 Challenges of Applying Intelligent Optimization
Algorithms in Manufacturing 64
2.6.1 Problem Modeling . 64
2.6.2 Algorithm Selection 65
2.6.3 Encoding Scheming 66
2.6.4 Operator Designing . 67
2.7 Future Approaches for Manufacturing Optimization 67
2.8 Future Requirements and Trends of Intelligent
Optimization Algorithm in Manufacturing . 68
2.8.1 Integration . 68
2.8.2 Configuration . 69
2.8.3 Parallelization 70
2.8.4 Executing as Service 71
viii Contents2.9 Summary 72
References . 74
Part II Design and Implementation
3 Dynamic Configuration of Intelligent
Optimization Algorithms 83
3.1 Concept and Mainframe of DC-IOA . 83
3.1.1 Mainframe of DC-IOA 84
3.1.2 Problem Specification and Construction
of Algorithm Library in DC-IOA . 85
3.2 Case Study 90
3.2.1 Configuration System for DC-IOA 90
3.2.2 Case Study of DC-IOA 93
3.2.3 Performance Analysis . 95
3.2.4 Comparison with Traditional Optimal Process 102
3.3 Summary 103
References . 104
4 Improvement and Hybridization of Intelligent
Optimization Algorithm . 107
4.1 Introduction 107
4.2 Classification of Improvement . 109
4.2.1 Improvement in Initial Scheme 109
4.2.2 Improvement in Coding Scheme 110
4.2.3 Improvement in Operator 112
4.2.4 Improvement in Evolutionary Strategy . 113
4.3 Classification of Hybridization . 114
4.3.1 Hybridization for Exploration 115
4.3.2 Hybridization for Exploitation . 116
4.3.3 Hybridization for Adaptation 117
4.4 Improvement and Hybridization Based on DC-IA 118
4.5 Summary 124
References . 124
5 Parallelization of Intelligent Optimization Algorithm 127
5.1 Introduction 127
5.2 Parallel Implementation Ways for Intelligent
Optimization Algorithm 131
5.2.1 Parallel Implementation Based
on Multi-core Processor . 131
5.2.2 Parallel Implementation Based
on Computer Cluster 132
Contents ix5.2.3 Parallel Implementation Based on GPU . 132
5.2.4 Parallel Implementation Based on FPGA 133
5.3 Implementation of Typical Parallel Topologies
for Intelligent Optimization Algorithm . 134
5.3.1 Master-Slave Topology 134
5.3.2 Ring Topology 136
5.3.3 Mesh Topology . 138
5.3.4 Full Mesh Topology 140
5.3.5 Random Topology . 140
5.4 New Configuration in Parallel Intelligent Optimization
Algorithm . 142
5.4.1 Topology Configuration in Parallelization
Based on MPI 144
5.4.2 Operation Configuration in Parallelization
Based on MPI 146
5.4.3 Module Configuration in Parallelization
Based on FPGA . 147
5.5 Summary 152
References . 152
Part III Application of Improved Intelligent
Optimization Algorithms
6 GA-BHTR for Partner Selection Problem 157
6.1 Introduction 157
6.2 Description of Partner Selection Problem
in Virtual Enterprise 160
6.2.1 Description and Motivation . 160
6.2.2 Formulation of the Partner Selection
Problem (PSP) 163
6.3 GA-BHTR for PSP . 165
6.3.1 Review of Standard GA . 165
6.3.2 Framewrok of GA-BHTR 166
6.3.3 Graph Generation for Representing
the Precedence Relationship Among PSP . 168
6.3.4 Distribute Individuals into Multiple Communities 172
6.3.5 Intersection and Mutation in GA-BHTR 175
6.3.6 Maintain Data Using the Binary Heap 177
6.3.7 The Catastrophe Operation . 179
6.4 Simulation and Experiment . 180
6.4.1 Effectiveness of the Proposed Transitive
Reduction Algorithm 181
6.4.2 Effectiveness of Multiple Communities . 182
x Contents6.4.3 Effectiveness of Multiple Communities
While Considering the DISMC Problem 183
6.4.4 Effectiveness of the Catastrophe Operation 184
6.4.5 Efficiency of Using the Binary Heap 184
6.5 Summary 187
References . 187
7 CLPS-GA for Energy-Aware Cloud Service Scheduling 191
7.1 Introduction 191
7.2 Related Works 193
7.3 Modeling of Energy-Aware Cloud Service Scheduling
in Cloud Manufacturing . 195
7.3.1 General Definition . 196
7.3.2 Objective Functions and Optimization Model . 198
7.3.3 Multi-Objective Optimization Model
for the Resource Scheduling Problem 200
7.4 Cloud Service Scheduling with CLPS-GA . 202
7.4.1 Pareto Solutions for MOO Problems . 202
7.4.2 Traditional Genetic Algorithms
for MOO Problems . 204
7.4.3 CLPS-GA for Addressing MOO Problems . 207
7.5 Experimental Evaluation . 211
7.5.1 Data and Implementation . 211
7.5.2 Experiments and Results . 213
7.5.3 Comparison Between TPCO and MPCO 214
7.5.4 Improvements Due to the Case Library . 217
7.5.5 Comparison Between CLPS-GA and Other
Enhanced GAs 218
7.6 Summary 221
References . 222
Part IV Application of Hybrid Intelligent Optimization Algorithms
8 SFB-ACO for Submicron VLSI Routing Optimization
with Timing Constraints . 227
8.1 Introduction 227
8.2 Preliminary 231
8.2.1 Terminology in Steiner Tree 231
8.2.2 Elmore Delay . 232
8.2.3 Problem Formulation . 233
8.3 SFB-ACO for Addressing MSTRO Problem . 237
8.3.1 ACO for Path Planning with Two Endpoints . 237
Contents xi8.3.2 Procedure for Constructing Steiner Tree
Using SFB-ACO 239
8.3.3 Constraint-Oriented Feedback in SFB-ACO 241
8.4 Implementation and Results . 243
8.4.1 Parameters Selection 243
8.4.2 Improvement of Synergy . 244
8.4.3 Effectiveness of Constraint-Oriented Feedback 249
8.5 Summary 254
References . 254
9 A Hybrid RCO for Dual Scheduling of Cloud Service
and Computing Resource in Private Cloud . 257
9.1 Introduction 257
9.2 Related Works 260
9.3 Motivation Example 261
9.4 Problem Description 263
9.4.1 The Modeling of DS-CSCR in Private Cloud . 263
9.4.2 Problem Formulation of DS-CSCR
in Private Cloud . 267
9.5 Ranking Chaos Algorithm (RCO) for DS-CSCR
in Private Cloud . 270
9.5.1 Initialization 271
9.5.2 Ranking Selection Operator . 271
9.5.3 Individual Chaos Operator 273
9.5.4 Dynamic Heuristic Operator 275
9.5.5 The Complexity of the Proposed Algorithm 277
9.6 Experiments and Discussions 277
9.6.1 Performance of DS-CSCR Compared
with Traditional Two-Level Scheduling . 280
9.6.2 Searching Capability of RCO for Solving
DS-CSCR . 280
9.6.3 Time Consumption and Stability of RCO
for Solving DS-CSCR . 283
9.7 Summary 285
References . 286
Part V Application of Parallel Intelligent Optimization Algorithms
10 Computing Resource Allocation with PEADGA 291
10.1 Introduction 291
10.2 Related Works 294
10.3 Motivation Example of OACR . 296
10.4 Description and Formulation of OACR . 297
xii Contents10.4.1 The Structure of OACR . 298
10.4.2 The Characteristics of CRs in CMfg . 300
10.4.3 The Formulation of the OACR Problem 301
10.5 NIA for Addressing OACR . 308
10.5.1 Review of GA, ACO and IA 308
10.5.2 The Configuration OfNIA for the OACR Problem . 311
10.5.3 The Time Complexity of the Proposed Algorithms . 314
10.6 Configuration and Parallelization of NIA 316
10.7 Experiments and Discussions 318
10.7.1 The Design of the Heuristic Information
in the Intelligent Algorithms 320
10.7.2 The Comparison of GA, ACO, IA and NDIA
for Addressing OACR . 322
10.7.3 The Performance of PNIA 326
10.8 Summary 328
References . 329
11 Job Shop Scheduling with FPGA-Based F4SA . 333
11.1 Introduction 333
11.2 Problem Description of Job Shop Scheduling . 335
11.3 Design and Configuration of SA-Based on FPGA . 335
11.3.1 FPGA-Based F4SA Design for JSSP . 335
11.3.2 FPGA-Based Operators of F4SA . 339
11.3.3 Operator Configuration Based on FPGA 344
11.4 Experiments and Discussions 344
11.5 Summary 346
References . 346
Part VI Future Works of Configurable Intelligent
Optimization Algorithm
12 Future Trends and Challenges 351
12.1 Related Works for Configuration of Intelligent
Optimization Algorithm 351
12.2 Dynamic Configuration for Other Algorithms 353
12.3 Dynamic Configuration on FPGA . 356
12.4 The Challenges on the Development of Dynamic
Configuration . 358
12.5 Summary 359
References . 360

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