Mathematical optimization is the selection of the best element in a set with respect to a given criterion. Optimization has become one of the most used tools in control theory to compute control laws, adjust parameters (tuning), estimate states, fit model parameters, find conditions in order to fulfill a given closed-loop property, among others. Optimization also plays an important role in the design of fault detection and isolation systems to prevent safety hazards and production losses that require the detection and identification of faults, as early as possible to minimize their impacts by implementing real-time fault detection and fault-tolerant systems. Recently, it has been proven that many optimization problems with convex objective functions and linear matrix inequality (LMI) constraints can be solved easily and efficiently using existing software, which increases the flexibility and applicability of the control algorithms. Therefore, real-world control systems need to comply with several conditions and constraints that have to be taken into account in the problem formulation, which represents a challenge in the application of the optimization algorithms. This book offers an overview of the state-of-the-art of the most advanced optimization techniques and their applications in control engineering.

In the current context of the electronic governance of society, both administrations and citizens are demanding greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled "Optimization for Decision Making". These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions, or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization for decision making in a coherent manner.

This book constitutes the refereed proceedings of the 16th International Conference on Learning and Intelligent Optimization, LION 16, which took place in Milos Island, Greece, in June 2022. The 36 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 60 submissions. LION deals with automatic solver configuration, parallel methods, intelligent optimization, nature-inspired algorithms, hard combinatorial optimization problems, DC learning, computational intelligence, and others. The contributions were organized in topical sections as follows: Invited Papers; Contributed Papers.

On-orbit operations optimization among multiple cooperative or noncooperative spacecraft, which is often challenged by tight constraints and shifting parameters, has grown to be a hot issue in recent years. The authors of this book summarize related optimization problems into four planning categories: spacecraft multi-mission planning, far-range orbital maneuver planning, proximity relative motion planning and multi-spacecraft coordinated planning. The authors then formulate models, introduce optimization methods, and investigate simulation cases that address problems in these four categories. This text will serve as a quick reference for engineers, graduate students, postgraduates in the fields of optimization research and on-orbit operation mission planning.