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.
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 Prims and
Dijkstras 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.
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.
