Metaheuristics for Combinatorial Optimization
Published on 13. February 2021
XI, 57 pages
978-3-030-68520-1 (ISBN)
Description
This book presents novel and original metaheuristics developed to solve the cost-balanced traveling salesman problem. This problem was taken into account for the Metaheuristics Competition proposed in MESS 2018, Metaheuristics Summer School, and the top 4 methodologies ranked are included in the book, together with a brief introduction to the traveling salesman problem and all its variants.
The book is aimed particularly at all researchers in metaheuristics and combinatorial optimization areas.
Key uses are metaheuristics; complex problem solving; combinatorial optimization; traveling salesman problem.
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XI, 57 p. 21 illus., 16 illus. in color.
File size
2,06 MB
ISBN-13
978-3-030-68520-1 (9783030685201)
DOI
10.1007/978-3-030-68520-1
Schweitzer Classification
Other editions
Additional editions
Salvatore Greco | Mario F. Pavone | El-Ghazali Talbi
Metaheuristics for Combinatorial Optimization
Book
02/2021
Springer
€192.59
Shipment within 7-9 days
Content
- Intro
- Editorial
- Contents
- Mixed Integer Programming Formulations for the Balanced Traveling Salesman Problem with a Lexicographic Objective
- 1 Introduction, Problem Description, and Preliminary Results
- 1.1 A Better MIP Formulation
- 1.2 Numerical Results
- 2 Impact on Problem Variants
- 2.1 Balanced Assignment
- 2.2 Balanced Fixed-Size Subset
- 2.3 TSP
- 2.4 Balanced Spanning Tree
- 2.5 Numerical Results on Sub-cases
- 3 Conclusion and Perspectives
- A Appendix
- Reference
- A Memetic Random Key Algorithm for the Balanced Travelling Salesman Problem
- 1 Introduction
- 2 The Memetic Random Key Algorithm
- 3 Computational Experiments
- 3.1 The Decoder Effect
- 3.2 The Local Search Frequency Effect
- 3.3 Solutions
- 4 Conclusions
- References
- A Variable Neighborhood Search Algorithm for Cost-Balanced Travelling Salesman Problem
- 1 Introduction
- 2 Cost-Balanced TSP
- 3 Proposed Methodology: A Variable Neighborhood Search Algorithm
- 4 Computational Experiments
- 4.1 Implementation
- 4.2 Computational Results
- 5 Conclusion and Discussion
- References
- Adaptive Iterated Local Search with Random Restarts for the Balanced Travelling Salesman Problem
- 1 Introduction
- 2 Problem Formulation
- 3 Methodology
- 3.1 Finding an Initial Solution
- 3.2 Adaptive Iterated Local Search with Random Restarts
- 4 Experiments
- 4.1 Instances
- 4.2 Parameters Tuning
- 4.3 Performance
- 5 Conclusion
- References
- Correction to: Metaheuristics for Combinatorial Optimization
- Correction to: S. Greco et al. (Eds.): Metaheuristics for Combinatorial Optimization, AISC 1332, https://doi.org/10.1007/978-3-030-68520-1
- Author Index
