Evolutionary Computation in Combinatorial Optimization

Name: Evolutionary Computation in Combinatorial Optimization | 12th European Conference, EvoCOP 2012, Málaga, Spain, April 11-13, 2012, Proceedings
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12th European Conference, EvoCOP 2012, Málaga, Spain, April 11-13, 2012, Proceedings

Jin-Kao Hao Martin Middendorf(Herausgeber*in)

Springer (Verlag)

1. Auflage

Erschienen am 23. März 2012

XII, 264 Seiten

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Inhalt

Title
Preface
Organization
Table of Contents
A Methodology for Comparing the Execution Time of Metaheuristics Running on Different Hardware
Introduction
Background
Classic Benchmarks
High Performance Benchmarks
Modern Benchmarks
Experimental Context
Algorithms and Problem
Hardware Platforms
Methodology
Obtaining the Data
Choosing a Benchmark
Fitting the Data
Predicting Running Times and Limitations
Conclusions and Further Work
References
A Variable Neighborhood Search Approach for the Two-Echelon Location-Routing Problem
Introduction
Related Work
Variable Neighborhood Search for the 2E-LRP
Experimental Results
Conclusions
References
An ILS-Based Metaheuristic for the Stacker Crane Problem
Introduction
SCP Definition and Notation
A Lower Bound to the SCP
A Metaheuristic Algorithm for the SCP
Multi-start Algorithm
Variable Neighborhood Descent Algorithm
Iterated Local Search
Computational Results
Conclusions
References
An NSGA-II Algorithm for the Green Vehicle Routing Problem
Introduction
Green Logistics
Emission Factors
Emission Estimation Techniques
The Vehicle Routing Problem with Emissions
Literature Review
The Bi-objective Green Vehicle Routing Problem
NSGA-II Algorithms for the Bi-objective GVRP
Implementation and Computational Results
Computational Results
Statistical Analysis
Conclusions
References
Clustering Search Heuristic for Solving a Continuous Berth Allocation Problem
Introduction
Literature Review
Problem Approach
Clustering Search (CS)
Computational Tests
Tuning Parameters
Results
Conclusions
References
Combining Heuristic and Exact Methods to Solve the Vehicle Routing Problem with Pickups, Deliveries and Time Windows
Introduction
Problem Formulation
Algorithm Operators
Construction Methods
Route Reconstruction Heuristics
Branch and Bound Method
Overall Algorithm
Experimental Results
Conclusions
References
D2MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance
Introduction
Multi-objective Particle Swarm Optimisation
D2MOPSO Approach
Experiments and Results
Experimental Setup
Quality Measures
Discussion and Conclusions
References
Domain Reduction Using GRASP Construction Phase for Transmission Expansion Planning Problem
Introduction
Transportation Model of the Transmission Expansion Planning Problem
Domain Reduction Using GRASP Construction Phase
Tests and Results
Garver System
Southern Brazilian System
North-Northeast Brazilian System
Conclusions
References
Electrical Load Management in Smart Homes Using Evolutionary Algorithms
Introduction
In-House Energy Management
Problem Description
Modeling the Problem
An Evolutionary Algorithm with Local Search
Solution Representation
Selection and Reproduction Scheme
Customized Search Operators
Local Search
Simulation Results
Conclusions and Outlook
References
Exact Computation of the Fitness-Distance Correlation for Pseudoboolean Functions with One Global Optimum
Introduction
Background
Fitness-Distance Correlation
Fitness-Distance Correlation for Elementary Landscapes
FDC, Autocorrelation Length and Local Optima
Conclusion
References
Genetic Algorithms for Scheduling Devices Operation in aWater Distribution System in Response to Contamination Events
Problem Description
Genetic Algorithms for the Scheduling of Operations
A Genetic Algorithm Based on Sequences
Two-Part Chromosome
A Genetic Algorithm Based on Activation Times
Computational Results
Conclusions
References
HyFlex: A Benchmark Framework for Cross-Domain Heuristic Search
Introduction
The HyFlex Framework
The ProblemDomain Class
The HyperHeuristic Class
Running a Hyper-heuristic
An Example Hyper-heuristic
HyFlex Problem Domains
HyFlex Achievements
Discussion and Future Work
References
Hyper-Heuristic Based on Iterated Local Search Driven by Evolutionary Algorithm
Introduction
Hyper-Heuristics
Proposed Hyper-Heuristic
Original ISEA Algorithm
ISEA Pseudo Code
ISEA Control Parameters' Setting
ISEA with Adapted Re-initialization Rate
Experiments
Experimental Setup
Results
Conclusions
References
Intensification/Diversification-Driven ILS for a Graph Coloring Problem
Introduction
Graph Coloring Problem
Definitions and Notations
Metaheuristic Approaches to the GCP
Intensification/Diversification-Driven ILS
Main Scheme of ID-ILS
Perturbation Step
Local Search Step
Experimental Results
Problem Instances and Experimental Protocol
Comparing the Different Perturbation Schemes
Comparison with Two Local Search Methods
Comparison with the Most Effective Algorithms
Analysis of the Parameters of ID-ILS
Conclusions
References
Iterated Greedy Algorithms for the Maximal Covering Location Problem
Introduction
Previous Work
Our Contribution
Paper Organization
Proposed IG Variants for the MLCP
The Probabilistic Greedy Procedure
PBIG+LNS
Computational Experiments
Problem Instances
Tuning Experiments
Experimental Results
Conclusions and Future Work
References
Multiobjectivizing the HP Model for Protein Structure Prediction
Introduction
Background and Notation
The HP Model for Protein Structure Prediction
Single-objective and Multiobjective Optimization
Multiobjectivization
Multiobjectivization Proposal: The Parity Decomposition
Experimental Setup
Algorithms
Test Cases and Performance Assessment
Results
Results for the (1+1) Evolutionary Algorithm
Results for the Genetic Algorithm
Conclusions and Future Work
References
Multi-Pareto-Ranking Evolutionary Algorithm
Introduction
Multi-Objective Evolutionary Algorithms (MOEAs) in Literature
Pareto-Based MOEAs
Tuning of MOEA Parameters
Constraint-Handling in MOEAs
A Multiple-Pareto-Ranking Genetic Algorithm
Ranking
Fitness Assignment
Selection Operator
Validation and Experimental Results
Tuning GAME's Parameters
Influence of Multiple Pareto Fronts
Performance Assessment
Conclusion
References
Pareto Local Search Algorithms for Anytime Bi-objective Optimization
Introduction
Anytime Pareto Local Search
Experimental Analysis
Experimental Setup and Performance Assessment
Experimental Results
Conclusions
References
Pure Strategy or Mixed Strategy? An Initial Comparison of Their Asymptotic Convergence Rate and Asymptotic Hitting Time
Introduction
Pure Strategy and Mixed Strategy EAs
Asymptotic Convergence Rate and Asymptotic Hitting Time
A Comparison of Pure Strategy and Mixed Strategy
Conclusion and Discussion
References
Recurrent Genetic Algorithms: Sustaining Evolvability
Introduction
Related Works
Recurrent Genetic Algorithms
Elitism
Experiments
NK Landscape
Hamming Centres
Continuous Optimisation
Conclusions
References
Splitting Method for Spatio-temporal Sensors Deployment in Underwater Systems
Introduction
Problem Presentation: Spatio-temporal Search Efforts Planning
The Solution Constraints
The Target Constraints
The Generalized Splitting Framework
Solving Our Real-World Problem
Evaluating the Detection Probability
The Splitting Algorithm
The Dedicated Gibbs Sampler
Illustrative Example: The Flaming Datum Search Problem
Conclusion and Prospects
References
The Vehicle Routing Problem with Backhauls: A Multi-objective Evolutionary Approach
Introduction
Multi-objective Combinatorial Optimization
Multi-objective EA for Solving VRPB and VRPSB
Experimental Study
Comparison with Previous Approaches
Multi-objective Performance
Conclusions
References
Author Index

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