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Benchmarks and Hybrid Algorithms in Optimization and Applications

Benchmarks and Hybrid Algorithms in Optimization and Applications
Author: Xin-She Yang
Publisher: Springer Nature
Total Pages: 250
Release: 2023-09-22
Genre: Technology & Engineering
ISBN: 9819939704

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This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications.


Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications

Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications
Author: Oscar Castillo
Publisher: Springer Nature
Total Pages: 383
Release: 2021-03-24
Genre: Technology & Engineering
ISBN: 3030687767

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We describe in this book, recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There also some papers that presents theory and practice of meta-heuristics in different areas of application. Another group of papers describe diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical applications. There are also some papers that present theory and practice of neural networks in different areas of application. In addition, there are papers that present theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, there are some papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition problems.


Extremal Optimization

Extremal Optimization
Author: Yong-Zai Lu
Publisher: CRC Press
Total Pages: 334
Release: 2018-09-03
Genre: Computers
ISBN: 1315362341

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Extremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors’ recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as "NP-hard" and the "phase transitions" occurring on the search landscape. Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO with a focus on self-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors’ recent research results. Finally, it discusses applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling. The authors present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. They also focus on the development of MEO and its applications. This book can be used as a reference for graduate students, research developers, and practical engineers who work on developing optimization solutions for those complex systems with hardness that cannot be solved with mathematical optimization or other computational intelligence, such as evolutionary computations.


Optimization Algorithms

Optimization Algorithms
Author: Ozgur Baskan
Publisher: BoD – Books on Demand
Total Pages: 326
Release: 2016-09-21
Genre: Mathematics
ISBN: 9535125923

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This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field. It consists of 13 chapters divided into two parts: (I) Engineering applications, which presents some new applications of different methods, and (II) Applications in various areas, where recent contributions of state-of-the-art optimization methods to diverse fields are presented.


A Comparison of Global Optimization Algorithms with Standard Benchmark Functions and Real-world Applications Using Energy Plus

A Comparison of Global Optimization Algorithms with Standard Benchmark Functions and Real-world Applications Using Energy Plus
Author:
Publisher:
Total Pages:
Release: 2009
Genre:
ISBN:

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There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimise the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility.


Optimization in Machine Learning and Applications

Optimization in Machine Learning and Applications
Author: Anand J. Kulkarni
Publisher: Springer Nature
Total Pages: 202
Release: 2019-11-29
Genre: Technology & Engineering
ISBN: 9811509948

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This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.


Optimization Techniques for Solving Complex Problems

Optimization Techniques for Solving Complex Problems
Author: Enrique Alba
Publisher: John Wiley & Sons
Total Pages: 504
Release: 2009-02-17
Genre: Computers
ISBN: 9780470411346

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Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One—covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two—delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.


Hybrid Algorithms for On-Line Search and Combinatorial Optimization Problems

Hybrid Algorithms for On-Line Search and Combinatorial Optimization Problems
Author: Yury V. Smirnov
Publisher:
Total Pages: 131
Release: 1997
Genre: Artificial intelligence
ISBN:

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Abstract: "By now Artificial Intelligence (AI), Theoretical Computer Science (CS theory) and Operations Research (OR) have investigated a variety of search and optimization problems. However, methods from these scientific areas use different problem descriptions, models, and tools. They also address problems with particular efficiency requirements. For example, approaches from CS theory are mainly concerned with the worst-case scenarios and are not focused on empirical performance. A few efforts have tried to apply methods across areas. Usually a significant amount of work is required to make different approaches 'talk the same language, ' be successfully implemented, and, finally, solve the actual same problem with an overall acceptable efficiency. This thesis presents a systematic approach that attempts to advance the state of the art in the transfer of knowledge across the above mentioned areas. In this work we investigate a number of problems that belong to or are close to the intersection of areas of interest of AI, OR and CS theory. We illustrate the advantages of considering knowledge available in different scientific areas and of applying algorthms [sic] across distinct disciplines through successful applications of novel hybrid algorithms that utilize benefitial [sic] features of known efficient approaches. Testbeds for such applications in this thesis work include both open theoretical problems and ones of significant practical importance. We introduce a representation change that enables us to question the relation between the Pigeonhole Principle and Linear Programming Relaxation. We show that both methods have exactly the same bounding power. Furthermore, even stronger relation appears to be between the two methods: The Pigeonhole Principle is the Dual of Linear Programming Relaxation. Such a relation explains the 'hidden magic' of the Pigeonhole Principle, namely its power in establishing upper bounds and its effectiveness in constructing optimal solutions. We also address various groups of problems, that arise in agent-centered search. In particular, we consider goal-directed exploration, in which search by a physical or fictitious agent with limited lookahead occurs in partially or completely unknown domains. The resulting Variable Edge Cost Algorithm (VECA) becomes the first method of solving goal-directed exploration problems that incorporates strong guidance from heuristic knowledge, yet is still capable of providing linear worst-case guarantees, even for complex search domains and misleading heuristics. This work aims at expanding the handset of AI tools that concern search efficiency and provides the foundation for further development of hybrid methods, cross-fertilization and successful applications across AI, CS theory, OR and other Computational Sciences."


Handbook of Formal Optimization

Handbook of Formal Optimization
Author: Anand J. Kulkarni
Publisher: Springer Nature
Total Pages: 1406
Release:
Genre:
ISBN: 9819738202

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Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance
Author: Vasant, Pandian M.
Publisher: IGI Global
Total Pages: 735
Release: 2012-09-30
Genre: Computers
ISBN: 1466620870

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Optimization techniques have developed into a significant area concerning industrial, economics, business, and financial systems. With the development of engineering and financial systems, modern optimization has played an important role in service-centered operations and as such has attracted more attention to this field. Meta-heuristic hybrid optimization is a newly development mathematical framework based optimization technique. Designed by logicians, engineers, analysts, and many more, this technique aims to study the complexity of algorithms and problems. Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.