Applied Multi-objective Optimization

The book explains basic ideas behind several kinds of applied multi-objective optimization and shows how it will be applied in practical contexts in the domain of healthcare, engineering design, and manufacturing. The book discusses how meta-heuristic algorithms are successful in resolving challenging, multi-objective optimization issues in various disciplines, including engineering, economics, medical and environmental management. The topic is useful for graduates, researchers and lecturers in optimization, engineering, management science and computer science.

Autorentext
Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He also holds a position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence , IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (SpringerNature), Data-Intensive Research (SpringerNature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He is an associate editor of IET Image Processing and editorial board member of Complex & Intelligent Systems, Springer Nature, Applied Soft Computing, Elsevier etc. He is having 35 authored booksand over 300 publications in the area of medical imaging, machine learning, computer aided diagnosis, data mining, etc. He is the Fellow of IETE and Senior member of IEEE.

Inhalt

Chapter 1 : An Introduction to Multi-objective Optimization using Meta-heuristic Algorithms: Techniques and Applications.- Chapter 2 : Counterfactual Explanations and Federated Learning for Multi-objective Optimization.- Chapter 3: Multi-objective Adaptive Guided Differential Evolution for Passively Controlled Structures Equipped with Tunned Mass Damper.- Chapter 4: Evolutionary Approaches for Multi-objective Optimization and Pareto-Optimal Solution Selection in Data Analytics.- Chapter 5 : Multi-objective Lichtenberg Algorithm for Optimum Design of Truss Structures.- Chapter 6 : Performance Analysis of Multi-objective Function-Based Fractional PID Controller for System Frequency Regulation .- Chapter 7: Multi-Modal Routing in Urban Transportation Networks using Multi-objective Quantum Particle Swarm.- Chapter 8 : Plant Leaf Disease Localization and Severity Measurement using Multi-objective Ant Colony Optimization.- Chapter 9 : Multi-objective Feature Selection: A Comprehensive Review.- Chapter 10 :Enhancing Feature Selection using Multi-objective Optimization Concept.