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. It presents various multi-objective combinatorial optimization issues that may benefit from different methods in theory and practice, as computational optimization has become increasingly popular due to more stringent design requirements in modern engineering practice.

Format: Paperback / softback
Length: 314 pages
Publication date: 15 February 2022
Publisher: Elsevier Science Publishing Co Inc

Multi-Objective Combinatorial Optimization Problems and Solution Methods delves into the outcomes of a recent breakthrough in multi-objective combinatorial optimization, encompassing a diverse range of approaches such as metaheuristic, mathematical programming, heuristic, hyper heuristic, and hybrid methodologies. In essence, the book offers a comprehensive exploration of various multi-objective combinatorial optimization challenges that can be addressed through a combination of theoretical and practical methods. These optimization problems arise across a wide spectrum of applications in fields such as operations research, engineering, biological sciences, and computer science, necessitating the development of diverse optimization techniques that bridge the gap between the discrete and continuous realms. This book serves as a valuable resource in addressing this critical topic, as computational optimization has gained increasing prominence due to its significance in design optimization and its applications in engineering and industry. The increasing demand for more stringent design requirements in modern engineering practice has further emphasized the importance of this field.

The book begins by providing an overview of the fundamental concepts and principles of multi-objective optimization, including the definition of optimization problems, objective functions, constraints, and search algorithms. It then delves into specific optimization techniques, such as genetic algorithms, simulated annealing, tabu search, and ant colony optimization, highlighting their advantages, limitations, and applications in various problem domains.

Furthermore, the book discusses the use of multi-objective optimization in real-world scenarios, such as portfolio optimization, resource allocation, and scheduling problems. It showcases case studies and practical examples to illustrate the effectiveness of different optimization approaches in addressing complex optimization problems.

In conclusion, Multi-Objective Combinatorial Optimization Problems and Solution Methods is a comprehensive and authoritative guide that provides a thorough understanding of multi-objective optimization techniques and their applications. It is an essential resource for researchers, practitioners, and students interested in this field, as it offers valuable insights and practical guidance for solving complex optimization problems across a wide range of industries.

Weight: 450g
Dimension: 235 x 191 (mm)
ISBN-13: 9780128237991