Multi-Objective Combinatorial Optimization Problems and Solution Methods

Multi-Objective Combinatorial Optimization Problems and Solution Methods discusses the results of a recent multi-objective combinatorial optimization achievement that considered metaheuristic, mathematical programming, heuristic, hyper heuristic and hybrid approaches. In other words, the 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, hence many optimization approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic and algebraic techniques.

This book covers this important topic as computational optimization has become increasingly popular as design optimization and its applications in engineering and industry have become ever more important due to more stringent design requirements in modern engineering practice.

• Presents a collection of the most up-to-date research, providing a complete overview of multi-objective combinatorial optimization problems and applications
• Introduces new approaches to handle different engineering and science problems, providing the field with a collection of related research not already covered in the primary literature
• Demonstrates the efficiency and power of the various algorithms, problems and solutions, including numerous examples that illustrate concepts and algorithms

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.

Dr Iman Rahimi is an experienced Machine Learning Engineer and Data Scientist with over a decade in AI, optimisation, and high-performance computing. He specialises in developing, deploying, and managing advanced ML models, including LLMs and NLP systems, using TensorFlow, PyTorch, and scikit-learn. His expertise extends to GPU-accelerated computing, cloud data platforms (AWS, Azure), and scalable MLOps practices with FastAPI, Flask, and Django. He has led research in energy systems, optimisation, and delivering high-impact solutions for industry and academia. Iman is widely published, with numerous high-impact journal articles and edited books in AI, operations research, and data analytics.