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Multi-Objective Combinatorial Optimization Problems and Solution Methods by Mehdi Toloo 9780128237991

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Description

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.

About the Author
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.


Book Information
ISBN 9780128237991
Author Mehdi Toloo
Format Paperback
Page Count 314
Imprint Academic Press Inc
Publisher Elsevier Science Publishing Co Inc
Weight(grams) 660g

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