This book focuses on recent advances in nonlinear analysis and optimization with important applications drawn from various fields, such as artificial intelligence, genetic algorithms, optimization ...problems under uncertainty, and fuzzy logic. Specifically, it is devoted to nonlinear problems associated with optimization which have some connection with applications. The ideas and techniques developed here will serve to stimulate further research in this dynamic field, and, in this way, the book will become a valuable reference for researchers, engineers and students in the field of mathematics, management science, operations research, optimal control science and economics.
This study focuses on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force. Traditional analytical optimisation method ...based on magnetic field with particle swarm optimisation algorithm was introduced to obtain the best combination of motor structure parameters. By contrast, the novel optimisation design method – Taguchi method based on orthogonal array was proposed to optimise the thrust and thrust ripple. After the design of experiments using finite-element analysis, the relative importance of each design parameter was estimated in detail. Experimental results of prototype can certify the superiority and validity of Taguchi optimisation method.
Optimization has been playing a key role in the design, planning and operation of chemical and related processes for nearly half a century. Although process optimization for multiple objectives was ...studied by several researchers back in the 1970s and 1980s, it has attracted active research in the last 10 years, spurred by the new and effective techniques for multi-objective optimization. In order to capture this renewed interest, this monograph presents the recent and ongoing research in multi-optimization techniques and their applications in chemical engineering.
Presents basic optimization principles and gradient-based algorithms to a general audience. This work pays attention to the difficulties - such as noise, discontinuities, expense of function ...evaluations, and the existence of multiple minima - that often unnecessarily inhibit the use of gradient-based methods.
A mechanism is a mathematical structure that models institutions through which economic activity is guided and coordinated. There are many such institutions; markets are the most familiar ones. ...Lawmakers, administrators and officers of private companies create institutions in order to achieve desired goals. They seek to do so in ways that economize on the resources needed to operate the institutions, and that provide incentives that induce the required behaviors. This book presents systematic procedures for designing mechanisms that achieve specified performance, and economize on the resources required to operate the mechanism. The systematic design procedures are algorithms for designing informationally efficient mechanisms. Most of the book deals with these procedures of design. When there are finitely many environments to be dealt with, and there is a Nash-implementing mechanism, our algorithms can be used to make that mechanism into an informationally efficient one. Informationally efficient dominant strategy implementation is also studied.
Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive ...search self-tunes and adapts, effectively learning by doing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics. Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics. Individual chapters cover reacting on the neighborhood, reacting on the annealing schedule, reactive prohibitions, model-based search, reacting on the objective function, relationships between reactive search and reinforcement learning, and much more. Each chapter is structured to show basic issues and algorithms, the parameters critical for the success of the different methods discussed, and opportunities and schemes for the automated tuning of these parameters. Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.
This comprehensive work examines important recent developments and modern applications in the fields of optimization, control, game theory and equilibrium programming. In particular, the concepts of ...equilibrium and optimality are of immense practical importance affecting decision-making problems regarding policy and strategies, and in understanding and predicting systems in different application domains, ranging from economics and engineering to military applications. The book consists of twenty-nine survey chapters written by distinguished researchers in the above areas.
Wing design optimization traditionally involves computationally expensive high-fidelity simulations, limiting the exploration of design spaces. In this study, we propose a methodology that combines ...low-fidelity numerical models with machine learning algorithms to efficiently navigate the complex parameter space of box-wing configurations. Through the utilisation of a surrogate model trained on a limited dataset derived from low-fidelity simulations, our method strives to predict results within an acceptable range, significantly curtailing computational costs and time. The effectiveness of this methodology is demonstrated through a series of case studies, involving the Onera M6 and NASA CRM wing as test cases and Bionica box-wing optimization as an application case. The initial application of the proposed methodology to the box-wing case successfully achieved an almost 9.82 % increase in overall aerodynamic efficiency. Its competitive performance compared to conventional optimization methods, along with its substantial reduction in computational time and resource requirements, is evident. This efficient methodology holds promise for enhancing the design optimization process for aviation start-ups by efficiently exploring complex design spaces with reduced computational burden.
Mobile robot path planning is an important part of the mobile robot field. The swarm intelligence optimisation algorithm has certain advantages in solving such multi-objective optimisation problems, ...so this Letter proposes to apply the pigeon-inspired optimisation (PIO) algorithm to the path planning problem. However, the traditional PIO algorithm is suffering from the problems of easy to get into local optimisation, low stability and premature convergence. To overcome its shortcomings, this Letter proposes a logistic beetle algorithm search–PIO (LBAS-PIO) algorithm. In the optimisation process of the LBAS-PIO algorithm, the PIO algorithm is initialised by the logistic mapping, making the search space more extensive. Learning from the idea of the beetle algorithm search, each pigeon has its own judgment of the environment and the number of iterations and search time are reduced. Further, the path evaluation function of the algorithm and the execution order of the operators are optimised to make the algorithm smoother and easier to find the global optimal solution. Simulation and experimental results illustrated the superiority of the LBAS-PIO algorithm and the LBAS-PIO algorithm better meets the needs of mobile robots for the optimal path planning.
