Discrete Cuckoo Search for Combinatorial Optimization (Springer Tracts in Nature-Inspired Computing)

✍ Scribed by Aziz Ouaarab

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 138
• Category: Library

✦ Synopsis

This book provides a literature review of techniques used to pass from continuous to combinatorial space, before discussing a detailed example with individual steps of how cuckoo search (CS) can be adapted to solve combinatorial optimization problems. It demonstrates the application of CS to three different problems and describes their source code. The content is divided into five chapters, the first of which provides a technical description, together with examples of combinatorial search spaces. The second chapter summarizes a diverse range of methods used to solve combinatorial optimization problems. In turn, the third chapter presents a description of CS, its formulation and characteristics. In the fourth chapter, the application of discrete cuckoo search (DCS) to solve three POCs (the traveling salesman problem, quadratic assignment problem and job shop scheduling problem) is explained, focusing mainly on a reinterpretation of the terminology used in CS and its source of inspiration. In closing, the fifth chapter discusses random-key cuckoo search (RKCS) using random keys to represent positions found by cuckoo search in the TSP and QAP solution space.

✦ Table of Contents

Preface
Acknowledgements
Contents
About the Author
Acronyms
1 Introduction
1.1 General Introduction
1.2 Challenges in Metaheuristics
1.3 Book Overview
1.4 Book Organization
References
Part I Theory and Formulations
2 Combinatorial Optimization Space
2.1 Technical Description of Combinatorial Space
2.2 Studied Combinatorial Optimization Problems
2.2.1 Traveling Salesman Problem
2.2.2 Job Shop Scheduling Problem
2.2.3 Quadratic Assignment Problem
2.3 Common Characteristics
2.3.1 Representation
2.3.2 Constraints
2.4 Conclusion
References
3 Solving Combinatorial Optimization Problems
3.1 COP Resolution Approaches
3.1.1 Vertical Improvement
3.1.2 Horizontal Improvement
3.2 Discretization
3.2.1 Discrete
3.2.2 Discreted
3.2.3 Projected
3.3 Conclusion
References
4 Cuckoo Search: From Continuous to Combinatorial
4.1 Cuckoo Search Description
4.2 Improved CS
4.3 Discrete CS
4.3.1 Nest
4.3.2 Egg
4.3.3 Objective Function
4.3.4 Search Space
4.4 Conclusion
References
Part II Application
5 DCS Applications
5.1 Main Functions
5.1.1 Get Cuckoo
5.1.2 Smart Cuckoo
5.1.3 Worst Cuckoo
5.2 TSP Adaptation
5.2.1 Egg and Nest
5.2.2 Objective Function
5.2.3 Search Space
5.2.4 Experimental Results
5.3 JSSP Adaptations
5.3.1 JSSP Solution
5.3.2 Objective Function
5.3.3 Search Space
5.3.4 Experimental Results
5.4 QAP Adaptations
5.4.1 QAP Solution
5.4.2 Objective Function
5.4.3 Search Space
5.4.4 Experimental Results
5.5 Conclusion
References
6 Random-Key Cuckoo Search (RKCS) Applications
6.1 RKCS
6.1.1 Solution Representation
6.1.2 Displacement
6.1.3 Lévy Flights
6.1.4 Main Functions
6.2 Application on TSP
6.2.1 TSP Solution
6.2.2 Displacement
6.2.3 Local Search and Neighborhood
6.2.4 Experimental Results
6.3 Application on QAP
6.3.1 QAP Solution
6.3.2 Displacement
6.3.3 Local Search
6.3.4 Experimental Results
6.4 Conclusion
References
Appendix A Benchmarks
A.1 TSP
A.2 JSSP
A.3 QAP
Appendix B Computer Codes
B.1 DCS for TSP
B.1.1 TSP
B.1.2 DCSTSP
B.1.3 DCS
B.2 DCS for JSSP
B.2.1 JSSP
B.2.2 DCSJSSP
B.2.3 DCS
B.3 DCS for QAP
B.3.1 QAP
B.3.2 DCSQAP
B.3.3 DCS
B.4 RKCS for TSP
B.4.1 TSP
B.4.2 CITY
B.4.3 CityList
B.4.4 Population
B.4.5 RKCSTSP
B.5 RKCS for QAP
B.5.1 QAP
B.5.2 Facility
B.5.3 Facility list
B.5.4 Population
B.5.5 RKCSQAP