Multi-Objective Combinatorial Optimization Problems and Solution Methods - Tapa blanda

Acerca de los autores

Dr. Mehdi Toloo is a Full Professor in the Faculty of Economics, Technical University of Ostrava, and Faculty of Business
Administration, University of Economics, Prague, Czech Republic. He received his Masters of Science in Applied Mathematics and
his Ph.D. in Operations Research. Dr. Toloo’s areas of interest include Operations Research, Decision Analysis, Performance
Evaluation, Multi-Objective Programming, and Mathematical Modelling. He has contributed to numerous international conferences
as a chair, keynote speaker, and member of the scientific committee. He is an area editor for the Elsevier journal Computers and
Industrial Engineering and an associate editor for RAIRO-Operations Research. His publications include the book Introduction to
Scientific Computing: 100 Problems and Solutions in Pascal and papers in top-tier journals such as Applied Mathematics and
Computers, Applied Mathematic Modeling, Expert Systems with Applications, and Computers and Mathematics with Applications.

Dr. Siamak Talatahari received his Ph.D degree in Structural Engineering from University of Tabriz, Iran. After graduation, he
joined the University of Tabriz where he is presently Professor of Structural Engineering. He is the author of more than 100 papers
published in international journals, 30 papers presented at international conferences and 8 international book chapters. Dr. Talatahari
has been recognized as Distinguished Scientist in the Ministry of Science and Technology and as Distinguished Professor at the
University of Tabriz. He also teaches at the Yakin Dogu University, Nicosia, Cyprus. In addition, he is a co-author with our author
Xin-She Yang of Swarm Intelligence and Bio-Inspired Computation: Structural Optimization Using Krill Herd Algorithm;
Metaheuristics in Water, Geotechnical and Transport Engineering, and Metaheuristic Applications in Structures and
Infrastructures, all published by as Insights by Elsevier.

Iman Rahimi, PhD, is a distinguished research scholar at the University of Technology Sydney, Australia, specializing in machine learning, optimization, and applied mathematics. He holds dual doctorates in Industrial Engineering and Computer Science, along with a BSc and MSc in Applied Mathematics. Dr. Rahimi has authored and edited several influential books, including titles on evolutionary computation and big data analytics, and has contributed extensively to academic literature as a reviewer for high-ranking journals. His editorial experience spans multiple publications, and he has received numerous international awards and research grants, highlighting his significant contributions to the field. With a robust background in operations research, Dr. Rahimi continues to advance knowledge in multiobjective optimization and its applications in various industries.

De la contraportada

Multi-Objective Combinatorial Optimization Problems and Solutions discuss the results of a recent multiobjective combinatorial optimization achievement considering metaheuristic, mathematical programming, heuristic, hyper heuristic, and hybrid approaches. In other words, this book presents various multi-objective combinatorial optimization issues that may benefit from different methods in theory and practice.

Combinatorial optimization problems appear in a wide range of applications in operations research, engineering, biological sciences and computer science. Many combinatorial optimization approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic, and algebraic techniques. Optimization problems with multi-objective arise in a natural fashion in most disciplines, and their solution has been a challenge to researchers for a long time. Despite the considerable variety of techniques developed in Operations Research (OR) and other disciplines to tackle these problems, the complexities of their solution calls for alternative approaches.

Computational optimization has become increasingly popular in recent years because design optimization and its applications in engineering and industry have become ever more important due to more stringent design requirements in modern engineering practice. In addition, design problems of interest and importance nowadays are often much harder to solve, as we intend to consider more realistic, large-scale, and nonlinear optimization problems under limited resources, money and time constraints.

From the metaheuristic point of view, there are several motivations for using these methods; for example, in metaheuristic, considering concavity or convexity is not needed. They also can produce a number of alternative solutions in a single run, which is another advantage of evolutionary algorithms. Additionally, from the point of combination, evolutionary algorithms (such as genetic algorithm) can integrate with certain decomposition algorithms. Additionally, there are some metaheuristics that are suitable for solving global optimization problems, including non-convex and discontinuous problems. Some metaheuristics are able to find a set of well-converged and diversified non-dominated solutions, known as Pareto solutions, in a single run as these algorithms perform better in dealing with some multi-objective optimization problems (MOOPs), such as huge search space, uncertainty, noise, and disjoint Pareto curves.

Conversely, for large-scale optimization problems, hybrid and decomposed methods are two categories of optimization methods which could find efficient solution for various problems. Moreover, mathematical approaches mostly possess strong algebra perception, and decision-makers are able to prove convergence of these algorithms analytically and can adjust the optimality gap precisely if required. The Editors present a variety of solutions and applications for Multi-Combinatorial Optimization problems in a way readers will find useful and illuminating for their own research and development.