Discrete Cuckoo Search for Combinatorial Optimization (Paperback)

Aziz Ouaarab

ISBN 10: 9811538387 / ISBN 13: 9789811538384

Published by Springer Verlag, Singapore, Singapore, 2021

About this Item

Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9789811538384

Bibliographic Details

• Title: Discrete Cuckoo Search for Combinatorial Optimization (Paperback)
• Publisher: Springer Verlag, Singapore, Singapore
• Publication Date: 2021
• Language: English
• ISBN 10: 9811538387
• ISBN 13: 9789811538384
• Binding: Paperback
• Condition: new

About this title

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.

About the Author

Dr. Aziz Ouaarab is an Assistant Professor of Computer Sciences and Applied Mathematics at Cadi Ayyad University of Marrakech, Morocco. Dr. Ouaarab received his M.S. degree in 2011 and his D.Phil. in Engineering Sciences in 2015, both from Mohammed V University of Rabat, Morocco. His thesis project focused on the use of nature-inspired metaheuristics to solve combinatorial optimization problems, and on cuckoo search as a special case to be improved. He joined the Computer Engineering and Systems Laboratory at the Faculty of Sciences and Techniques, Marrakech, in 2019. His research chiefly focuses on combinatorial optimization problems and how they can be solved by means of swarm intelligence and nature-inspired metaheuristics. Dr. Ouaarab currently serves on the editorial board of the International Journal of Bio-Inspired Computation.