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The handbook of heuristics consists of five main parts: search strategies, local search, metaheuristics, analysis and implementations, and applications. They cover from search methods and methodological aspects, such as matheuristics, the exciting field in which mathematical programming is combined with heuristics, to applications that provide the practitioner with a description of some relevant optimization issues in a number of specific application areas, such as scheduling, vehicle routing, or network optimization.
The first edition of the Handbook of Heuristics was published in 2018 and contained 47 chapters. In this second edition, the authors revised 30 of them to include new developments in the area that appeared in the last few years. In particular, the reader may find 14 chapters in search strategies, including a new chapter on deep learning, 4 in local search, 14 in metaheuristics, 5 in analysis and implementations, and 24 in applications. The inclusion of 14 new chapters makes this second edition even more comprehensive, totaling 61 chapters.
List of contents
Adaptive and Multi-Level Metaheuristics.- Biased Random-Key Genetic Programming.- Data Mining in Heuristics.- Deep Learning in Search Heuristics.- Evolution Strategies.- Evolutionary Algorithms.- Innovative Applications of Metaheuristics to Supervised Machine Learning.- Matheuristics by Examples.- Multi-start Methods.- Multiobjective Optimization.- Quantum-Inspired Heuristics.- Restart Strategies.- Simheuristics.- The Hybrid Metaheuristic CMSA.- Constraint-Based Local Search.- Guided Local Search.- Theory of Local Search.
About the author
Rafael Martí is a Professor of Statistics and Operations Research at the University of Valencia, Spain. He earned his PhD in Mathematics from the same university in 1994. His research focuses extensively on metaheuristics for complex optimization problems. Prof. Martí has published over 200 papers, most of them in indexed journals (JCR), including the
European Journal of Operational Research,
INFORMS Journal on Computing,
IIE Transactions,
Journal of Global Optimization,
Computers & Operations Research, and
Discrete Applied Mathematics. He is the co-author of several monographic books, the most recent being
Discrete Diversity and Dispersion Maximization (Springer, 2023) and
Exact and Heuristic Methods in Combinatorial Optimization (Springer-Verlag, 2022).
Prof. Martí serves as Area Editor for the
Journal of Heuristics and as Associate Editor for the
European Journal of Operational Research and
Mathematical Programming Computation. He is also a senior research scientist at the Spanish logistics consulting firm OGA and holds a U.S. patent. He has delivered around 50 invited and plenary talks and is a recurring visiting professor at the University of Colorado (USA), Molde University College (Norway), and University College Dublin (Ireland). He has participated in over 20 government-funded research projects, and his publications have received more than 15,000 citations.
Panos Pardalos was born in Drosato (Mezilo) Argitheas GR in 1954 and graduated from Athens University (Department of Mathematics). He received his PhD (Computes and Information Sciences) from the University of Minnesota. He is an Emeritus Distinguished 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. Since 2011 he has been the academic advisor at LATNA, HSE.
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 the 2013 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.”
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.
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.
Summary
The handbook of heuristics consists of five main parts: search strategies, local search, metaheuristics, analysis and implementations, and applications. They cover from search methods and methodological aspects, such as matheuristics, the exciting field in which mathematical programming is combined with heuristics, to applications that provide the practitioner with a description of some relevant optimization issues in a number of specific application areas, such as scheduling, vehicle routing, or network optimization.
The first edition of the Handbook of Heuristics was published in 2018 and contained 47 chapters. In this second edition, the authors revised 30 of them to include new developments in the area that appeared in the last few years. In particular, the reader may find 14 chapters in search strategies, including a new chapter on deep learning, 4 in local search, 14 in metaheuristics, 5 in analysis and implementations, and 24 in applications. The inclusion of 14 new chapters makes this second edition even more comprehensive, totaling 61 chapters.
