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E-raamat: Evolutionary Computation for Dynamic Optimization Problems

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This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time.

Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.

Part I Fundamentals
1 Evolutionary Dynamic Optimization: Test and Evaluation Environments
3(36)
Shengxiang Yang
Trung Thanh Nguyen
Changhe Li
1.1 Introduction
3(2)
1.2 DOPs: Concepts, Brief Review, and Classification
5(3)
1.2.1 Concepts of DOPs
5(1)
1.2.2 Dynamic Test Problems: Brief Review
5(1)
1.2.3 Major Characteristics and Classification of DOPs
6(2)
1.3 Typical Dynamic Test Problems and Generators
8(8)
1.3.1 Dynamic Test Problems in the Real Space
8(2)
1.3.2 Dynamic Test Problems in the Binary Space
10(3)
1.3.3 Dynamic Test Problems in the Combinatorial Space
13(3)
1.4 Performance Metrics
16(9)
1.4.1 Optimality-Based Performance Measures
16(5)
1.4.2 Behaviour-Based Performance Measures
21(3)
1.4.3 Discussion
24(1)
1.5 The Generalized Dynamic Benchmark Generator (GDBG)
25(6)
1.5.1 Dynamic Rotation Peak Benchmark Generator
27(1)
1.5.2 Dynamic Composition Benchmark Generator
28(1)
1.5.3 Dynamic Test Problems for the CEC 2009 Competition
29(2)
1.6 Conclusions and Discussions
31(8)
References
32(7)
2 Evolutionary Dynamic Optimization: Methodologies
39(26)
Trung Thanh Nguyen
Shengxiang Yang
Juergen Branke
Xin Yao
2.1 Introduction
39(1)
2.2 Optimization Approaches
40(13)
2.2.1 The Goals of EDO Algorithms
40(1)
2.2.2 Detecting Changes
41(1)
2.2.3 Introducing Diversity When Changes Occur
42(2)
2.2.4 Maintaining Diversity during the Search
44(2)
2.2.5 Memory Approaches
46(2)
2.2.6 Prediction Approaches
48(2)
2.2.7 Self-adaptive Approaches
50(1)
2.2.8 Multi-population Approaches
51(2)
2.3 Theoretical Development of EDO Methodologies
53(2)
2.4 Summary and Future Research Directions
55(10)
2.4.1 Summary
55(1)
2.4.2 The Gaps between Academic Research and Real-World Problems
55(1)
2.4.3 Future Research Directions
56(1)
References
57(8)
3 Evolutionary Dynamic Optimization: Challenges and Perspectives
65(20)
Philipp Rohlfshagen
Xin Yao
3.1 Introduction
65(1)
3.2 Challenge I: Problem Definition
66(5)
3.2.1 Optimization in Uncertain Environments
66(2)
3.2.2 Problem Definitions
68(1)
3.2.3 Characterisation of Dynamics
69(1)
3.2.4 Problem Properties, Assumptions and Generalisations
70(1)
3.3 Challenge II: Benchmark Problems
71(4)
3.3.1 Benchmark Problems
71(1)
3.3.2 Combinatorial Fitness Landscapes
72(1)
3.3.3 Real-World Dynamics
73(1)
3.3.4 Experimental Settings
74(1)
3.4 Challenge III: Notions of Optimality
75(4)
3.4.1 Performance Measures in Evolutionary Dynamic Optimization
75(2)
3.4.2 Existence of a Model
77(1)
3.4.3 Notions of Optimality
77(2)
3.5 Implications, Perspectives and Conclusions
79(6)
3.5.1 Summary
79(1)
3.5.2 Implications and Perspectives
80(1)
3.5.3 Conclusions
80(1)
References
81(4)
4 Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art
85(24)
Carlo Raquel
Xin Yao
4.1 Introduction
85(1)
4.2 Comprehensive Definition of Dynamic Multi-objective Optimization
86(2)
4.3 Dynamic Multi-objective Test Problems
88(2)
4.3.1 Dynamic Multi-objective Optimization Test Problems
90(1)
4.4 Performance Measures
90(7)
4.4.1 Performance Measures for Problems with Known Pareto Front
92(3)
4.4.2 Performance Measures for Problems with Unknown Pareto Fronts
95(2)
4.5 Dynamic Multi-objective Optimization Approaches
97(6)
4.5.1 Diversity Introduction
97(2)
4.5.2 Diversity Maintenance
99(1)
4.5.3 Multiple Populations
100(1)
4.5.4 Prediction-Based Approaches
101(1)
4.5.5 Memory-Based Approaches
102(1)
4.6 Summary and Future Works
103(6)
References
104(5)
Part II Algorithm Design
5 A Comparative Study on Particle Swarm Optimization in Dynamic Environments
109(28)
Changhe Li
Shengxiang Yang
5.1 Introduction
109(1)
5.2 PSO in Dynamic Environments
110(8)
5.2.1 Particle Swarm Optimization
110(1)
5.2.2 PSO in Dynamic Environments
111(7)
5.3 Discussions and Suggestions
118(3)
5.3.1 Issues with Current Schemes
118(2)
5.3.2 Future Algorithms for DOPs
120(1)
5.4 Experimental Study
121(11)
5.4.1 Experimental Setup
122(2)
5.4.2 Effect on Varying the Shit Length
124(2)
5.4.3 Effect on Varying the Number of Peaks
126(2)
5.4.4 Effect on Varying the Number of Dimensions
128(2)
5.4.5 Comparison in Hard-to-Detect Environments
130(2)
5.5 Conclusions
132(5)
References
133(4)
6 Memetic Algorithms for Dynamic Optimization Problems
137(34)
Hongfeng Wang
Shengxiang Yang
6.1 Introduction
137(2)
6.2 Investigated Algorithms
139(9)
6.2.1 Framework of GA-Based Memetic Algorithms
139(1)
6.2.2 Local Search
140(3)
6.2.3 Adaptive Learning Mechanism in Multiple LS Operators
143(2)
6.2.4 Diversity Maintaining
145(2)
6.2.5 Balance between Local Search and Diversity Maintaining
147(1)
6.3 Dynamic Test Environments
148(2)
6.4 Experimental Study
150(14)
6.4.1 Experimental Design
150(2)
6.4.2 Experimental Study on the Effect of LS Operators
152(3)
6.4.3 Experimental Study on the Effect of Diversity Maintaining Schemes
155(4)
6.4.4 Experimental Study on Comparing the Proposed Algorithm with Several Peer GAs on DOPs
159(5)
6.5 Conclusions and Future Work
164(7)
References
168(3)
7 BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems
171(22)
Enrique Alba
Hajer Ben-Romdhane
Saoussen Krichen
Briseida Sarasola
7.1 Introduction
172(1)
7.2 Statement of the Problem
173(1)
7.3 The Proposed Approach: BIPOP-Algorithm
174(5)
7.3.1 Working Principles of BIPOP
175(3)
7.3.2 Construction of BIPOP
178(1)
7.3.3 Functions Utilized in the Algorithms
179(1)
7.4 Computational Experiments
179(10)
7.4.1 Experimental Framework
179(1)
7.4.2 Analysis
180(9)
7.5 Conclusions
189(4)
References
189(4)
8 Evolutionary Optimization on Continuous Dynamic Constrained Problems - An Analysis
193(28)
Trung Thanh Nguyen
Xin Yao
8.1 Introduction
193(1)
8.2 Characteristics of Real-World Dynamic Constrained Problems
194(1)
8.3 A Real-Valued Benchmark to Simulate DCOPs Characteristics
195(5)
8.3.1 Related Literature
195(1)
8.3.2 Generating Dynamic Constrained Benchmark Problems
196(1)
8.3.3 A Dynamic Constrained Benchmark Set
196(4)
8.4 Challenges to Solve DCOPs
200(14)
8.4.1 Analysing the Performance of Some Common Dynamic Optimization Strategies in Solving DCOPs
200(2)
8.4.2 Chosen Algorithms and Experimental Settings
202(5)
8.4.3 Experimental Results and Analyses
207(6)
8.4.4 Suggestions to Improve Current Dynamic Optimization Strategies in Solving DCOPs
213(1)
8.5 Conclusion and Future Research
214(7)
References
215(6)
Part III Theoretical Analysis
9 Theoretical Advances in Evolutionary Dynamic Optimization
221(20)
Philipp Rohlfshagen
Per Kristian Lehre
Xin Yao
9.1 Introduction
221(1)
9.2 Evolutionary Dynamic Optimization
222(2)
9.2.1 Optimization Problems
222(1)
9.2.2 Optimization in Uncertain Environments
223(1)
9.2.3 Evolutionary Algorithms
224(1)
9.3 Theoretical Foundation
224(7)
9.3.1 Introduction to Runtime Analysis
224(2)
9.3.2 Runtime Analysis for Dynamic Functions
226(1)
9.3.3 No Free Lunches in the Dynamic Domain
227(1)
9.3.4 Benchmark Problems
228(3)
9.4 Runtime Analysis for Dynamic Functions
231(4)
9.4.1 First Hitting Times for Pattern Match
231(1)
9.4.2 Analysis of Frequency and Magnitude of Change
232(2)
9.4.3 Tracking the Optimum in a Lattice
234(1)
9.5 Conclusions
235(6)
9.5.1 Summary and Implications
235(1)
9.5.2 Future Work
236(1)
References
237(4)
10 Analyzing Evolutionary Algorithms for Dynamic Optimization Problems Based on the Dynamical Systems Approach
241(28)
Renato Tinos
Shengxiang Yang
10.1 Introduction
241(1)
10.2 Exact Model of the GA in Stationary Environments
242(3)
10.3 Dynamic Optimization Problems
245(4)
10.4 Examples
249(16)
10.4.1 The XOR DOP Generator
249(4)
10.4.2 The Dynamic Environment Generator Based on Problem Difficulty
253(3)
10.4.3 The Dynamic 0-1 Knapsack Problem
256(9)
10.5 Conclusion and Future Work
265(4)
References
265(4)
11 Dynamic Fitness Landscape Analysis
269(30)
Hendrik Richter
11.1 Introduction
269(2)
11.2 Dynamic Fitness Landscapes: Definitions and Properties
271(8)
11.2.1 Introductory Example: The Moving Peaks
271(2)
11.2.2 Definition of Dynamic Fitness Landscapes
273(3)
11.2.3 Dynamics and Fitness Landscapes
276(3)
11.3 Analysis Tools for Dynamic Fitness Landscapes
279(7)
11.3.1 Analysis of Topological Properties
280(3)
11.3.2 Analysis of Dynamical Properties
283(3)
11.4 Numerical Experiments
286(7)
11.5 Conclusion
293(6)
References
294(5)
12 Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms
299(18)
Iulia Maria Comsa
Crina Grosan
Shengxiang Yang
12.1 Introduction
299(1)
12.2 Dynamic Optimization
300(2)
12.3 Multi-objective Aspect
302(2)
12.4 The Multi-objective Subset Sum Problem
304(1)
12.5 Analysis of the Dynamic Multi-objective Subset Sum Problem
304(5)
12.5.1 Algorithm Description
305(1)
12.5.2 Numerical Results and Discussions
306(3)
12.6 Conclusions
309(8)
References
312(5)
Part IV Applications
13 Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem
317(26)
Michalis Mavrovouniotis
Shengxiang Yang
13.1 Introduction
317(2)
13.2 Dynamic Travelling Salesman Problem with Traffic Factor
319(1)
13.2.1 DTSP with Random Traffic
319(1)
13.2.2 DTSP with Cyclic Traffic
320(1)
13.3 Ant Colony Optimization for the DTSP
320(3)
13.3.1 Standard ACO
321(1)
13.3.2 Population-Based ACO (P-ACO)
322(1)
13.3.3 React to Dynamic Changes
322(1)
13.4 Investigated ACO Algorithms with Immigrants Schemes
323(5)
13.4.1 General Framework of ACO with Immigrants Schemes
323(2)
13.4.2 ACO with Random Immigrants
325(1)
13.4.3 ACO with Elitism-Based Immigrants
325(1)
13.4.4 ACO with Hybrid Immigrants
326(1)
13.4.5 ACO with Memory-Based Immigrants
326(1)
13.4.6 ACO with Environmental-Information Immigrants
327(1)
13.5 Experiments
328(10)
13.5.1 Experimental Setup
328(1)
13.5.2 Parameter Settings
329(1)
13.5.3 Experimental Results and Analysis of the Investigated Algorithms
329(6)
13.5.4 Experimental Results and Analysis of the Investigated Algorithms with Other Peer ACO
335(3)
13.6 Conclusions and Future Work
338(5)
References
339(4)
14 Genetic Algorithms for Dynamic Routing Problems in Mobile Ad Hoc Networks
343(34)
Hui Cheng
Shengxiang Yang
14.1 Introduction
343(3)
14.2 Related Work
346(2)
14.2.1 Shortest Path Routing
346(1)
14.2.2 Multicast Routing
347(1)
14.3 Network and Problem Models
348(2)
14.3.1 Mobile Ad Hoc Network Model
348(1)
14.3.2 Dynamic Shortest Path Routing Problem Model
348(1)
14.3.3 Dynamic Multicast Routing Problem Model
349(1)
14.4 Specialized GAs for the Routing Problems
350(4)
14.4.1 Specialized GA for the Shortest Path Routing Problem
350(2)
14.4.2 Specialized GA for the Multicast Routing Problem
352(2)
14.5 Investigated GAs for the Dynamic Routing Problems
354(3)
14.5.1 Traditional GAs
354(1)
14.5.2 GAs with Immigrants Schemes
354(1)
14.5.3 Improved GAs with Immigrants Schemes
355(1)
14.5.4 GAs with Memory Schemes
356(1)
14.5.5 GAs with Memory and Immigrants Schemes
356(1)
14.6 Experimental Study
357(15)
14.6.1 Dynamic Test Environment
357(1)
14.6.2 Experimental Study for the DSPRP
357(7)
14.6.3 Experimental Study for the DMRP
364(8)
14.7 Conclusion
372(5)
References
372(5)
15 Evolutionary Computation for Dynamic Capacitated Arc Routing Problem
377(26)
Yi Mei
Ke Tang
Xin Yao
15.1 Introduction
378(2)
15.2 Problem Definition
380(6)
15.2.1 Static Capacitated Arc Routing Problem
380(1)
15.2.2 Dynamic Capacitated Arc Routing Problem
381(5)
15.3 Evolutionary Computation for Dynamic Capacitated Arc Routing Problem
386(7)
15.3.1 Addressing the Capacitated Arc Routing Problem Issues
386(6)
15.3.2 Tackling the Dynamic Environment
392(1)
15.4 Benchmark for Dynamic Capacitated Arc Routing Problem
393(3)
15.5 Preliminary Investigation of the Fitness Landscape
396(2)
15.6 Conclusion
398(5)
References
399(4)
16 Evolutionary Algorithms for the Multiple Unmanned Aerial Combat Vehicles Anti-ground Attack Problem in Dynamic Environments
403(30)
Xingguang Peng
Shengxiang Yang
Demin Xu
Xiaoguang Gao
16.1 Introduction
404(1)
16.2 Intelligent Online Path Planning (OPP)
405(8)
16.2.1 Formulation of the OPP Problem
406(1)
16.2.2 Problem-Solving Approach: LP-DMOEA
407(3)
16.2.3 Decision-Making on the Selection of Executive Solution
410(3)
16.3 Dynamic Target Assignment
413(7)
16.3.1 Formulation of the Dynamic WTA Problem
413(3)
16.3.2 Problem-Solving Approach: Memory-Based Estimation of Distribution Algorithm with Environment Identification
416(4)
16.3.3 Chromosome Representation
420(1)
16.3.4 Weapon-UCAV Mapping
420(1)
16.4 Simulation Results and Analysis
420(9)
16.4.1 Simulation Scenario
420(3)
16.4.2 Results and Analysis on the Intelligent OPP Problem
423(4)
16.4.3 Results and Analysis on the Dynamic WTA Problem
427(2)
16.5 Conclusions and Future Work
429(4)
References
430(3)
17 Advanced Planning in Vertically Integrated Wine Supply Chains
433(32)
Maksud Ibrahimov
Arvind Mohais
Maris Ozols
Sven Schellenberg
Zbigniew Michalewicz
17.1 Introduction
433(2)
17.2 Literature Review
435(5)
17.2.1 Supply Chain Management
436(2)
17.2.2 Time-Varying Constraints
438(1)
17.2.3 Computational Intelligence
439(1)
17.3 Wine Supply Chain
440(4)
17.3.1 Maturity Models
442(1)
17.3.2 Vintage Intake Planning
443(1)
17.3.3 Crushing
443(1)
17.3.4 Tank Farm
444(1)
17.3.5 Bottling
444(1)
17.4 Vintage Intake Planning
444(3)
17.4.1 Description of the Problem
444(2)
17.4.2 Constraints
446(1)
17.5 Tank Farm
447(6)
17.5.1 Description of the Problem
447(2)
17.5.2 Functionality
449(3)
17.5.3 Results
452(1)
17.6 Bottling
453(7)
17.6.1 Time-Varying Challenges in Wine Bottling
455(2)
17.6.2 Objective
457(1)
17.6.3 The Algorithm
457(3)
17.7 Conclusion
460(5)
References
462(3)
Author Index 465(2)
Subject Index 467
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