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Construct, Merge, Solve & Adapt : A Hybrid Metaheuristic for Combinatorial Optimization / by Christian Blum.
Springer Nature - Springer Computer Science eBooks 2024 English International Available online
View online- Format:
- Book
- Author/Creator:
- Blum, C. (Christian), author.
- Series:
- Computational Intelligence Methods and Applications, 2510-1773
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Computational intelligence.
- Computer science.
- Operations research.
- Management science.
- Computer simulation.
- Artificial Intelligence.
- Computational Intelligence.
- Theory of Computation.
- Operations Research, Management Science.
- Computer Modelling.
- Local Subjects:
- Artificial Intelligence.
- Computational Intelligence.
- Theory of Computation.
- Operations Research, Management Science.
- Computer Modelling.
- Physical Description:
- 1 online resource (202 pages)
- Edition:
- 1st ed. 2024.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
- Summary:
- This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver. Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem. The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics.
- Contents:
- Introduction to CMSA
- Self-Adaptive CMSA
- Adding Learning to CMSA
- Replacing Hard Mathematical Models with Set Covering Formulations
- Application of CMSA in the Presence of Non-Binary Variables
- Additional Research Lines Concerning CMSA.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 9783031601033
- OCLC:
- 1443164874
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