Handbook of Formal Optimization

2024
ISBN: 978-981-97-3819-9
Verlag: Springer Nature Singapore

Buch, Englisch, 1426 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 2779 g

The formal optimization handbook is a comprehensive guide that covers a wide range of subjects. It includes a literature review, a mathematical formulation of optimization methods, flowcharts and pseudocodes, illustrations, problems and applications, results and critical discussions, and much more. The book covers a vast array of formal optimization fields, including mathematical and Bayesian optimization, neural networks and deep learning, genetic algorithms and their applications, hybrid optimization methods, combinatorial optimization, constraint handling in optimization methods, and swarm-based optimization. This handbook is an excellent reference for experts and non-specialists alike, as it provides stimulating material. The book also covers research trends, challenges, and prospective topics, making it a valuable resource for those looking to expand their knowledge in this field.

Zielgruppe

Research

Autoren/Hrsg.

Gandomi, Amir H.

Kulkarni, Anand J.

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung

Inhaltsverzeichnis

Robust Optimization of Discontinuous Loss Functions.- Robust Conjugate Gradient Methods for Non-smooth Convex Optimization and Image Processing Problems.- Solving Cropping Pattern Optimization Problems Using Robust Positive Mathematical Programming.- Optimal Allocation of Groundwater Resources in the Agricultural Sector Under Restrictive Policies on Groundwater Extraction.- Incorporating Nelder-Mead Simplex as an Accelerating Operator to Improve the Performance of Metaheuristics in Nonlinear System Identification.- A Discrete Cuckoo Search Algorithm for the Cumulative Capacitated Vehicle Routing Problem.- Commonly Used Static and Dynamic Single-Objective Optimization Benchmark Problems.- Evolutionary Multi-objective Optimization of Hyperparameters for Decision Support in Healthcare.- Combination of Cooperative Grouper Fish -- Octopus Algorithm and DBSCAN to Automatic Clustering.- Multi-population Evolutionary and Swarm Intelligence Dynamic Optimization Algorithms: A Survey.- Solving Vehicle Routing Problem Using a Hybridization of Gain-Based Ant Colony Optimization and Firefly Algorithms.- Impact of Local Search in the Memetic Particle Swarm Optimization.- Salp Swarm Algorithm for Optimization of Shallow Foundations.- Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings.- Memory-Driven Metaheuristics: Improving Optimization Performance.- Synergistic Collaboration of Motion-Based Metaheuristics for the Strength Prediction of Cement-Based Mortar Materials Using TSK Model.- Positron-Enabled Atomic Orbital Search Algorithm for Improved Reliability-Based Design Optimization.- Steganography Based on Fuzzy Edge Detection, Cohort Intelligence and Thresholding.- Classification of Emotions in Ambient Assisted Living Environment using Machine Learning Approaches.- Optimization and Machine Learning Algorithms for Intelligent Microwave Sensing: A Review.- Machine Learning Algorithms for Autonomous Vehicles.- High-Resolution Remote Sensing Image Classification with Kernel Linear Discriminant Analysis.- Neural Networks and Deep Learning.- Deep Learning in Stock Market: Techniques, Purpose, and Challenges.- DNN Approach to Obtain BER vs SNR for Spatial Modulation System.- Steel Plate Fault Detection Using the Fitness-Dependent Optimizer and Neural Networks.- Dynamic Intelligence of Self-Organized Map in the Frequency-Based Optimum Design of Structures.- Combination of Bagging and Neural Network for Improving Precipitation Estimates Using Remote Sensing Data.- Robust Optimization of PTO Settings for Point Absorber Wave Energy Converter.- T-adaptive an Online Tuning Technique Coupled to MOEA/D Algorithm: A Comparative Analysis with Offline Parameter Tuning Techniques.- Cohort Intelligence-based Multi-objective Optimizer.- Deep Learning for Solving Loading, Packing, Routing, and Scheduling Problems.- Solving the Pallet Loading Problem with Deep Reinforcement Learning.- A Variant of Parallel-Hybrid Genetic Algorithm for Large-Scale Traveling Salesman Problem.- Variable Neighborhood Search for Cost Function Networks.- Competitive Game Table and the Optimization Algorithm.- A Comprehensive Review of Goal Programming Problems and Constraint Handling Approaches.- A Comprehensive Review of Patient Scheduling Techniques with Uncertainty.- Solving the 0-1 Knapsack Problem using LAB Algorithm.- Genetic Algorithms and Applications.- Multi-objective Genetic Algorithms.- Bilinear Fuzzy Genetic Algorithm and Its Application on the Optimum Design of Steel Structures with Semi-rigid Connections.- Variants of the Genetic Algorithm on Load Frequency Control Application.- Explaining Optimisation of Offshore Wind Farms Using Metaheuristics.- Optimization of Concrete Chimneys Considering Random Underground Blast and Temperature Effects.- Gear Material Selection Using an Integrated PSI-MOORA Method.- Heuristics: An Overview.- A Brief Review of Bilevel Optimization Techniques and Their Applications.- Mastering the Cosmos: Leveraging Optimization Methods for Advances in Space Exploration.- Solving the Total Weighted Earliness Tardiness Blocking Flowshop Scheduling Problem.- Solving Multiple Traveling Salesmen Problem Using Prim’s and Dijkstra’s Algorithms: A Case Study on Emergency Medical Supplies.- Mechanical Machining Process Optimization.- Energy-Efficient Manufacturing Scheduling: A Systematic Literature Review.- A Socio-Physics-Based Hybrid Metaheuristic for Solving Complex Non-convex Constrained Optimization Problems.- Overcoming Constraints: The Critical Role of Penalty Functions as Constraint-Handling Methods in Structural Optimization.

Über Autor(innen)

Anand J Kulkarni holds a PhD in Artificial Intelligence (AI) based Distributed Optimization from Nanyang Technological University, Singapore, MS in AI from the University of Regina, Canada. He worked as a Postdoctoral Research Fellow at Odette School of Business, University of Windsor, Canada. Anand has a Bachelor of Engineering in Mechanical Engineering from the Shivaji University, India, and holds a Diploma from the Board of Technical Education, Mumbai, India. Since 2021, he has been working as a Research Professor and Associate Director of the Institute of Artificial Intelligence at the MITWPU, Pune, India. His research interests include AI-based nature-inspired optimization algorithms and self-organizing systems. Anand pioneered optimization methodologies such as Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, Socio Evolution & Learning Optimization Algorithm, Leader-Advocate-Believer Algorithm, and Snail Homing and Mating Search Algorithm. Anand has published over 80 research papers in peer-reviewed reputed journals, chapters, and conferences along with 7 authored and 15 edited books. He has so far guided 6 doctoral, 10 masters, and over 100 UG students. Anand is the lead series editor for Springer and Taylor & Francis as well as associate editor of Elsevier journals such as ‘Engineering Applications of Artificial Intelligence’ and ‘Systems and Soft Computing’ as well as IOS Press KES journal. He is the recipient of the best paper award in IEEE ICNSC, Chicago, USA, and 'The Swatantry Veer Savarkar Award' 2023 by ‘Pune Marathi Granthalay’, Pune for his Marathi book entitled 'Artificial Intelligencechya Watewar'.

Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. Before joining UTS, Prof. Gandomi was an Assistant Professor at the Stevens Institute of Technology and a distinguished research fellow at BEACON Center, Michigan State University. Prof. Gandomi has published 400+ journal papers and 14 books. He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are data analytics and global optimization (big) in real-world problems in particular.