Handbook of Formal Optimization
Unknown authors
Abstract
Optimization carries great significance in both human affairs and the laws of nature.It refers to a positive and intrinsically human concept of minimization or maximization to achieve the best or most favorable outcome from a given situation.Besides, as the resources are becoming scarce, there is a need to develop new methods and techniques and modify the existing ones which will make the systems extract maximum from minimum use of these resources, i.e., maximum utilization of available resources with minimum investment or cost of any kind.The resources could be any, such as land, materials, machines, personnel, skills, time, etc.This handbook discusses background/literature reviews and optimization method descriptions in detail, including mathematical formulations, illustrations, problems, and applications.The handbook also provides state-of-the-art solutions and results with critical discussions, flowcharts/pseudocodes, etc.It serves as a complete reference for UG and PG students, academicians, industry researchers, and practitioners.Basic knowledge and understanding of mathematics are required to understand the concepts and formulations as well as the nuances of optimization methods.In order to cover most of the aspects of the optimization methods and applications, the handbook is divided into 16 distinct sections.The sections are associated with mathematical optimization/programming, evolutionary optimization-based methods, swarm-based optimization, physics-based optimization, socio-inspired based optimization, machine learning, neural networks and deep learning, multi/many-objective optimization, hybrid optimization methods, goal programming problems and methods, combinatorial optimization, genetic algorithms and applications, engineering optimization, optimization in management, optimization in manufacturing processes, and constraint handling in optimization methods.The contributions in these sections are received from all parts of the world.The contributions not only cover the deterministic optimization methodologies, but also approximation-based methods such as genetic algorithms, cohort intelligence, particle swarm optimization method, evolutionary algorithms, atomic orbital search algorithm, cuckoo search algorithm, ant colony optimization method, salp swarm algorithm, fish-octopus algorithm, as well as several hybrid algorithms.The problems solved are from a variety of domains, including single-objective and v vi Preface multi-objective problems.In addition, the handbook addresses a variety of problems such as the knapsack problem, steel structure design and steel plate fault detection problem, steganography, image processing, optimal allocation of groundwater resources, healthcare decision support system, vehicle routing problem, control systems in tall buildings and load frequency control problem, manufacturing scheduling, machining process problems, gear material selection, etc.Several other important problems were also addressed, including benchmark test cases.Moreover, the critical review of the heuristic methods, bilevel optimization methods, scheduling problems, machine learning algorithms, goal programming problems, and patient scheduling techniques have been discussed.Furthermore, several constrainthandling methods and their applications have been critically reviewed.Editors of the handbook thank the experts for investing sincere efforts and time in reviewing and helping significantly improve the quality of the submitted chapters.