Handbook of Heuristics

1. Matheuristics

   • Martina Fischetti, Matteo Fischetti

2. Memetic Algorithms

   • Carlos Cotta, Luke Mathieson, Pablo Moscato

3. Metaheuristics for Medical Image Registration

   • Andrea Valsecchi, Enrique Bermejo, Sergio Damas, Oscar Cordón

4. Metaheuristics for Natural Gas Pipeline Networks

   • Roger Z. Rı́os-Mercado

5. Multi-objective Optimization

   • Carlos A. Coello Coello

6. Multi-start Methods

   • Rafael Martí, Jose A. Lozano, Alexander Mendiburu, Leticia Hernando

7. Network Optimization

   • Luciana S. Buriol

8. Optimization Problems, Models, and Heuristics in Wireless Sensor Networks

   • Vinicius Morais, Fernanda S. H. Souza, Geraldo R. Mateus

9. Parallel Meta-heuristic Search

   • Teodor Gabriel

10. Particle Swarm Methods

    • Konstantinos E. Parsopoulos

11. Particle Swarm Optimization for the Vehicle Routing Problem: A Survey and a Comparative Analysis

    • Yannis Marinakis, Magdalene Marinaki, Athanasios Migdalas

12. POPMUSIC

    • Éric D. Taillard, Stefan Voß

13. Random-Key Genetic Algorithms

    • José Fernando Gonçalves, Mauricio G. C. Resende

14. Restart Strategies

    • Oleg V. Shylo, Oleg A. Prokopyev

15. Scatter Search

    • Rafael Martı́, Ángel Corberán, Juanjo Peiró

16. Scheduling Heuristics

    • Rubén Ruiz

17. Selected String Problems

    • Christian Blum, Paola Festa

18. Supply Chain Management

    • Helena Ramalhinho Lourenço, Martı́n Gómez Ravetti

19. Tabu Search

    • Manuel Laguna

20. The Maximum Clique and Vertex Coloring

    • Oleksandra Yezerska, Sergiy Butenko

The second edition will comprise at least 15 new chapters surrounding methodologies and applications. As a start, the following new topics were proposed:

Methodologies: Automatic algorithm selection, Automatic algorithm tuning, Machine learning, Path Relinking, Simulated annealing, Quantum computing.

Applications: Airline optimization, Artificial intelligence, Cloud computing, Electrical power systems, Finance, Order batching, Pharmaceutical, Production, Vehicle routing.

Additionally, authors of existing ‘chapters’ will be contacted to encourage them to revise/update their entries.



**From the first edition**
Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a singlemethod. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as ‘rules of thumb’ but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.

• Algorithms
• Heuristics
• Metaheuristics
• Search Strategies
• local search
• supply chain
• logistics
• routing
• optimization
• algorithms

• Department of Statistics and Operations, University of Valencia, Valencia, Spain

  Rafael Martí

• University of Florida Industrial and Systems Engineering, Gainesville, USA

  Pardalos Panos

• Amazon.com, Inc+University of Washington, Seattle, USA

  Mauricio G. C. Resende

Rafael Martí is Professor of Statistics and Operations Research at the University of Valencia, Spain. He received a doctoral degree in Mathematics from the University of Valencia in 1994. He has done extensive research in metaheuristics for hard optimization problems. Dr. Martí has more than 200 publications, and about half of them are in indexed journals (JCR), including EJOR, Informs JoC, IIE Transactions, JOGO, C&OR, ESWA, and Discrete and Applied Maths. He is the co-author of several books, being the two more recent ones edited by Springer "Exact and Heuristics Methods in Combinatorial Optimization" (2022) and "Discrete Diversity and Dispersion Maximization" (2023). Prof. Martí has secured an American patent, and he is currently Area Editor in the Journal of Heuristics, Associate Editor in the Math. Prog. Computation, TOP, and the Int. Journal of Metaheuristics. He is Senior Research Associate of OptTek Systems (USA), and has given about 50 invited and plenary talks. Dr. Martí has been invited Professor at the University of Colorado (USA), University of Molde (Norway), University College of Dublin (Ireland), and University of Bretagne-Sud (France). He coordinates the Spanish Network on Metaheuristics, currently funded as a SEIO working group.

Panos Pardalos was born in Greece and graduated from Athens University (Department of Mathematics). He received his PhD (Computes and Information Sciences) from the University of Minnesota. He is a Distinguished Emeritus Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science  Information  Engineering departments.
Panos Pardalos is a world-renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data Sciences. He is a Fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS and was awarded the2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Panos  Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for “scientific contributions that stand the test of time.” Since 2011 he has been the academic advisor of the Laboratory of Algorithms and Technologies for Network Analysis (LATNA), NRU HSE.
Panos Pardalos has been awarded a prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher’s entire achievements to date – fundamental discoveries, new theories, insights that have had significant impact on their discipline.
Panos Pardalos is also a Member of several  Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, Computational Management Science, and Springer Nature Operations Research Forum. He has published over 600 journal papers, and edited/authored over 200 books. He is one of the most cited authors and has graduated 71 PhD students so far. Details can be found in www.ise.ufl.edu/pardalos.

Mauricio G.C. Resende grew up in Rio de Janeiro (BR), West Lafayette (IN-US), and Amherst (MA-US). He did his undergraduate training in electrical engineering (systems engineering concentration) at the Pontifical Catholic U. of Rio de Janeiro.  He obtained an MS in operations research from Georgia Tech and a PhD in operations research from the U. of California, Berkeley.  He is most known for his work with metaheuristics, in particular GRASP and biased random-key genetic algorithms, as well as for his work with interior point methods for linear programming and network flows.  Dr. Resende has published over 200 papers on optimization and holds 15 U.S. patents.  He has edited four handbooks, including the Handbook of Heuristics, the Handbook of Applied Optimization, and the Handbook of Optimization in Telecommunications, and is coauthor of the book “Optimization by GRASP.” He sits on the editorial boards of several optimization journals. Prior to joining Amazon.com in 2014 as a Principal Research Scientist in the transportation area, Dr. Resende was a Lead Inventive Scientist at the Mathematical Foundations of Computing Department of AT&T Bell Labs and at the Algorithms and Optimization Research Department of AT&T Labs Research in New Jersey. Now retired from Amazon, Dr. Resende is an Affiliate Professor of Industrial and Systems Engineering at the University of Washington in Seattle and a visiting professor at universities around the world.

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