Heuristics for Optimization and Learning

Preface
Contents
1 Process Plan Generation for Reconfigurable Manufacturing Systems: Exact Versus Evolutionary-Based Multi-objective Approaches
1.1 Introduction
1.2 Literature Review
1.3 Problem Description and Mathematical Formulation
1.3.1 Problem Description
1.3.2 Mathematical Formulation
1.4 Proposed Approaches
1.4.1 Iterative Multi-Objective Integer Linear Program (I-MOILP)
1.4.2 Adapted Archived Multi-Objective Simulated-Annealing (AMOSA)
1.4.3 Adapted Non Dominated Sorting Genetic Algorithm II (NSGA-II)
1.5 Experimental Results and Analyses
1.5.1 Experimental Scheme 1
1.5.2 Experimental Scheme 2
1.6 Conclusion
References
2 On VNS-GRASP and Iterated Greedy Metaheuristics for Solving Hybrid Flow Shop Scheduling Problem with Uniform Parallel Machines and Sequence Independent Setup Time
2.1 Introduction
2.2 Description of the Hybrid Flow Shop Problem
2.3 Resolution
2.3.1 Initialization Heuristics
2.3.2 Metaheuristics
2.4 Numerical Simulation
2.4.1 Simulation Instances
2.4.2 Experimental Results
2.5 Conclusion
References
3 A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with Makespan Criterion
3.1 Introduction
3.2 Problem Formulation
3.3 Energy-Efficient VBIH Algorithm
3.3.1 Initial Population
3.3.2 Energy-Efficient Block Insertion Procedure
3.3.3 Energy-Efficient Insertion Local Search
3.3.4 Energy-Efficient Uniform Crossover and Mutation
3.3.5 Archive Set
3.4 Computational Results
3.5 Conclusions
References
4 Solving 0-1 Bi-Objective Multi-dimensional Knapsack Problems Using Binary Genetic Algorithm
4.1 Introduction
4.2 Literature Review
4.3 Problem Formulation
4.4 Bi-Objective BGA
4.5 Computational Results
4.6 Conclusion
References
5 An Asynchronous Parallel Evolutionary Algorithm for Solving Large Instances of the Multi-objective QAP
5.1 Introduction
5.2 Related Works
5.3 The APM-MOEA Model
5.3.1 Global Search View of the Organizer
5.3.2 Asynchronous Communications
5.3.3 Control Islands
5.3.4 Local Search
5.4 Experimental Results
5.4.1 Performance Metrics
5.4.2 The GISMOO Algorithm
5.4.3 MQAP Instances
5.4.4 Experimental Conditions
5.4.5 Resolution of Small MQAP Instances
5.4.6 Resolution of Large MQAP Instances
5.5 Conclusion
References
6 Learning from Prior Designs for Facility Layout Optimization
6.1 Introduction
6.2 Related Work
6.3 Facility Layout Model
6.4 Similarity Model
6.4.1 Probabilistic Layout Model
6.4.2 Estimation
6.5 Similarity in Layout Optimization
6.6 Experiments
6.7 Discussion
References
7 Single-Objective Real-Parameter Optimization: Enhanced LSHADE-SPACMA Algorithm
7.1 Enhanced LSHADE with Semi-parameter Adaptation Hybrid with CMA-ES (ELSHADE-SPACMA)
7.1.1 LSHADE Algorithm
7.1.2 CMA-ES Algorithm
7.1.3 Semi-parameter Adaptation of Scaling Factor (F) and Crossover Rate (Cr)
7.1.4 LSHADE-SPACMA Algorithm
7.1.5 AGDE Mutation Strategy
7.1.6 ELSHADE-SPACMA Hybridization Framework
7.2 Experimental Study
7.2.1 Numerical Benchmarks
7.2.2 Parameter Settings and Involved Algorithms
7.3 Experimental Results and Discussions
7.3.1 Results of ELSHADE-SPACMA Algorithm
7.4 Conclusion
References
8 Operations Research at Bulk Terminal: A Parallel Column Generation Approach
8.1 Introduction
8.2 Overview of Bulk Optimization
8.3 Integrated Production Planning and Scheduling
8.4 Literature Review
8.5 Integrated Production Planning and Scheduling
8.6 Column Generation Method
8.6.1 Parallel Approach
8.6.2 Primal Heuristic
8.7 Computational Experiments
8.8 Final Remarks
References
9 Heuristic Solutions for the (α, β)-k Feature Set Problem
9.1 Introduction
9.2 Problem Statement
9.3 Building an Instance of (α, β)-k FSP
9.4 The Proposed Solution Method
9.4.1 Obtaining the Minimum Number of Features
9.4.2 Obtaining the Maximum Value of β
9.5 Computational Results
9.6 Conclusion
References
10 Generic Support for Precomputation-Based Global Routing Constraints in Local Search Optimization
10.1 Introduction
10.2 Background
10.2.1 Constraint-Based Local Search, the OscaR Way
10.2.2 Sequence Variables
10.2.3 Cross-Product of Neighbourhoods
10.2.4 Conventions for Modeling VRP
10.3 A Generic Routing Global Constraint
10.3.1 Template for Precomputation-Based Global Constraints
10.3.2 Route Length with Asymmetric Distance Matrix
10.3.3 Keeping Track of Segments and Precomputations
10.3.4 Segment-Based API
10.3.5 Logarithmic Reduction of Quadratic Precomputations
10.4 A Global Constraint for Drone Autonomy
10.5 Global Time Window Constraint
10.6 Related Work
10.7 Conclusion and Perspectives
References
11 Dynamic Simulated Annealing with Adaptive Neighborhood Using Hidden Markov Model
11.1 Introduction
11.2 Literature Review
11.3 The Proposed Approach
11.3.1 Viterbi Algorithm
11.3.2 Baum Welch Algorithm
11.4 Experiments
11.4.1 Experimental Setup
11.4.2 Numerical Results
11.4.3 Comparison of Convergence Performance
11.4.4 Statistical Analysis
11.5 Conclusion and Future Research
References
12 Hybridization of the Differential Evolution Algorithm for Continuous Multi-objective Optimization
12.1 Introduction
12.2 Literature: DE Algorithms for MO Problems
12.2.1 The de Algorithm: Basic Notions
12.2.2 NSDE ch12Iorio04
12.2.3 DEMO ch12Robic05
12.2.4 ADE-MOIA ch12Lin15
12.3 Hybridization Between de and IWO: IWODEMO
12.3.1 The Weed Analogy
12.3.2 IWODEMO
12.4 Numerical Experiments
12.4.1 Instances: DTLZ and ZDT
12.4.2 Metrics
12.4.3 Experimental Conditions and Parameters
12.5 Results and Analysis
12.5.1 Results for the DTLZ Instances
12.5.2 Results for the ZDT Instances
12.5.3 Analysis
12.6 Conclusion
References
13 A Steganographic Embedding Scheme Using Improved-PSO Approach
13.1 Introduction
13.2 Particle Swarm Optimization
13.3 Steganographic Scheme Based Improved-PSO
13.3.1 General Description
13.3.2 Improved-PSO Embedding Scheme
13.4 Experimental Setup
13.4.1 Experimental Case 1
13.4.2 Experimental Case 2
13.4.3 Experimental Case 3
13.5 Conclusion
References
14 Algorithms Towards the Automated Customer Inquiry Classification
14.1 Introduction
14.2 Related Works
14.3 Methods
14.3.1 Preprocessing Phase
14.3.2 Training/Testing Phase
14.4 Data Description
14.5 Empirical Analysis
14.6 Optimization with Neural Networks
14.7 Conclusion
14.8 Future Work
References
15 An Heuristic Scheme for a Reaction Advection Diffusion Equation
15.1 Introduction
15.2 New Optimized Domain Decomposition Methods
15.2.1 Optimized Domain Decomposition Methods
15.2.2 The OO2 Method
15.2.3 New Domain Decomposition Method with Two Iteration (AlgDF)
15.3 Evolutionary Algorithm for PDE
15.4 Numerical Results
15.5 Conclusion
References
16 Stock Market Speculation System Development Based on Technico Temporal Indicators and Data Mining Tools
16.1 Introduction
16.2 Related Works
16.3 Proposed Search Algorithm
16.4 Experimental Results
16.5 Conclusion and Perspectives
References
17 A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System
17.1 Introduction
17.2 Related Work
17.3 Proposed Method
17.3.1 Hidden Markov Model
17.4 Experimental Results and Comparison
17.4.1 Comparison on the Solution Accuracy
17.4.2 Comparison on the Convergence Speed
17.4.3 Statistical Test
17.5 Conclusion
References
18 A New Cut-Based Genetic Algorithm for Graph Partitioning Applied to Cell Formation
18.1 Introduction
18.2 Formulation
18.2.1 Input data
18.2.2 Flow Graph Construction
18.2.3 Decision Variables
18.2.4 Intermediate Processing
18.2.5 Constraints
18.2.6 Objective Function
18.3 Theoretic Preliminaries
18.4 The Cut-Based GA
18.4.1 Principles of the Genetic Algorithm
18.4.2 GA Implementation
18.5 Computational Results
18.6 Conclusions
References
19 Memetic Algorithm and Evolutionary Operators for Multi-Objective Matrix Tri-Factorization Problem
19.1 Introduction
19.2 Multi-Objective Non-Negative Matrix Tri-Factorization Problem
19.3 Classical Evolutionary Operators
19.4 Naive Approach
19.5 Memetic Algorithm
19.5.1 Evolutionary Algorithm
19.6 Experiments
19.7 Results
19.8 Conclusion and Future Work
References
20 Quaternion Simulated Annealing
20.1 Introduction
20.2 Background
20.2.1 Neighborhood Structure
20.2.2 Quaternions
20.3 Simulated Annealing Based Quaternions
20.4 Experimental Results
20.4.1 Benchmark Functions
20.4.2 Comparison of the Convergence Speed
20.4.3 Performance Comparison with Other Optimization Algorithms
20.4.4 Statistical Test
20.5 Conclusion
References
21 A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model
21.1 Introduction
21.2 Literature Review
21.3 Cooperative Multi-swarm Conception of PSO
21.3.1 Standard PSO
21.3.2 Sub-swarms Constitution
21.3.3 Sub-swarms Parameters Adaptation
21.3.4 Multi-swarms Cooperation
21.4 Experimentation
21.4.1 Parameters Setting
21.4.2 Performance Evaluation
21.4.3 Statistical Tests
21.5 Conclusion
References
22 Experimental Sensitivity Analysis of Grid-Based Parameter Adaptation Method
22.1 Introduction
22.2 Background Information
22.2.1 Differential Evolution
22.2.2 Grid-Based Parameter Adaptation Method
22.3 Experimental Analysis
22.4 Conclusion
References
23 Auto-Scaling System in Apache Spark Cluster Using Model-Based Deep Reinforcement Learning
23.1 Introduction
23.2 Background
23.2.1 Apache Spark on OpenStack
23.3 Methodology
23.3.1 Feature Selection
23.3.2 Applied DQN for Auto-Scaling Task
23.3.3 Auto-Scaling System Design
23.4 Evaluation
23.5 Conclusion
References
24 Innovation Networks from Inter-organizational Research Collaborations
24.1 Introduction
24.2 Related Work
24.3 Network Generation with Linkage Threshold
24.3.1 Description of the Algorithm
24.3.2 Complexity Analysis
24.4 Experiment Setup
24.4.1 Dataset
24.4.2 Network Metrics
24.5 Network Analysis
24.6 Discussion
24.7 Conclusion
References
25 Assessing Film Coefficients of Microchannel Heat Sinks via Cuckoo Search Algorithm
25.1 Introduction
25.2 Heat Transfer Problem
25.2.1 Design Heat Transfer Problem
25.2.2 Objective of the Direct Heat Transfer Problem
25.2.3 Objective of the Inverse Heat Transfer Problem
25.3 Cuckoo Search Algorithm
25.4 Methodology
25.5 Results and Discussion
25.6 Conclusions
References
26 One-Class Subject Authentication Using Feature Extraction by Grammatical Evolution on Accelerometer Data
26.1 Introduction
26.2 Related Work
26.3 Preliminaries
26.3.1 Data-Set Description
26.3.2 Data Preparation
26.3.3 Training, Validation and Test Sets
26.4 Evolutionary System
26.4.1 System Overview
26.4.2 Grammar
26.5 Experiment Design
26.5.1 Baselines
26.5.2 Run Parameters
26.6 Results and Discussion
26.6.1 Frequently Selected Sub-sequences and Functions
26.7 Conclusions
References
27 Semantic Composition of Word-Embeddings with Genetic Programming
27.1 Introduction
27.2 Learning Word Embeddings Using Neural Networks
27.2.1 Generation of the Embeddings
27.3 A Genetic Programming Approach for Word-Embedding Composition
27.4 Problem Benchmark: Word Analogy Task
27.5 Description of the GP Approach
27.5.1 GP Operators
27.5.2 Fitness Function
27.6 Experiments
27.6.1 Numerical Results
27.6.2 Evaluating Answers and Evolved Programs
27.6.3 Comparison of the Different Fitness Functions
27.7 Conclusions and Future Work
References
28 New Approach for Continuous and Discrete Optimization: Optimization by Morphological Filters
28.1 Introduction
28.2 A Brief Overview of Existing Metaheuristic Algorithm
28.3 Optimization by Morphological Filter
28.3.1 Inspiration
28.4 Results and Discussion
28.4.1 Real Programming Problems
28.4.2 Integer Programming Problem
28.5 Conclusion
References
Appendix Index
Appendix Index
Index