Catalogue Search | MBRL
هل أنت متأكد أنك تريد إزالة الكتاب من الرف؟
{{itemTitle}}
532,433
نتائج ل
"Optimization"
صنف حسب:
Iterative optimizers : difficulty measures and benchmarks
Almost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.
Book
Robust optimization
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution.
eBook
A survey on evolutionary computation for complex continuous optimization
Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including
M
any-dimensions,
M
any-changes,
M
any-optima,
M
any-constraints, and
M
any-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including
reducing problem difficulty
,
increasing algorithm diversity
,
accelerating convergence speed
,
reducing running time
, and
extending application field
. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.
Journal Article
Structural Behaviour on Slope Number of Jahangir Graphs
Abstract
The slope number is defined to be the minimum number of slopes required to draw the graph. In the present paper, investigation on slope number of Jahangir graph is focused and studied elaborately, since the defined graph has an excellent application on transmitting confidential information between nuclear sites. In this paper, slope number of Jahangir graph J
2,m
is determined and studied elaborately. This is an optimization problem and is NP-hard to determine for any arbitrary graph.
Journal Article
An adaptive Gradient-type Method for Composite Optimization Problems with Gradient Dominance Condition and Generalized Smoothness1
We consider an interesting class of composite optimization problems with a gradient dominance condition and introduce corresponding analogue of the recently proposed concept of an inexact oracle. This concept is applied to some classes of smooth functional.
Journal Article