книга New Optimization Algorithms and their Applications: Atom-Based, Ecosystem-Based and Economics-Based

А Б В Г Д Е Ж З И К Л М Н О П Р С Т У Ф Х Ц Ч Ш Щ Э Ю Я
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New Optimization Algorithms and their Applications: Atom-Based, Ecosystem-Based and Economics-Based

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Название: New Optimization Algorithms and their Applications: Atom-Based, Ecosystem-Based and Economics-Based
Автор: Zhenxing Zhang, Liying Wang
Страниц: 180
Формат: PDF, EPUB
Размер: 31.5 MB
Качество: Отличное
Язык: Английский
Год издания: 2021


New Optimization Algorithms and Applications: Atom-Based, Ecosystem-Based and Economics-Based presents the development of three new optimization algorithms - an Atom Search Optimization (ASO) algorithm, an Artificial Ecosystem-Based Optimization algorithm (AEO), a Supply Demand Based Optimization (SDO), and their applications within engineering. These algorithms are based on benchmark functions and typical engineering cases. The book describes the algorithms in detail and demonstrates how to use them in engineering.The title verifies the performance of the algorithms presented, simulation results are given, and MATLAB® codes are provided for the methods described.



Over seven chapters, the book introduces ASO, AEO and SDO, and presents benchmark functions, engineering problems, and coding. This volume offers technicians and researchers engaged in computer and intelligent algorithm work and engineering with one source of information on novel optimization algorithms.



Optimization algorithms are increasingly popular in intelligent computing and are widely applied to a large number of real-world engineering problems. Their popularity derives from the following aspects. First, all of these optimization techniques have some fundamental theories and mathematical models that have been proved to be reasonable, which come from the real world and are inspired by all types of physical phenomena or biological behaviors. The theories about these optimization algorithms are simple and easy to understand. Second, these optimization algorithms can be thought of as a black box. This means that given a set of inputs, these algorithms can easily provide a set of outputs for any optimization problem. They are very flexible and versatile because one can change the structures and parameters of the algorithms to obtain better solutions. Third, metaheuristic algorithms can effectively avoid local optima, which is very valuable for addressing engineering problems which are typically considered as multimodal functions. In addition, one can develop their variants by absorbing the merits of other algorithms to improve the accuracy of solutions within a reasonable time. Fourth, the metaheuristic optimization algorithms can tackle different types of problems including, but not limited to, single-objective and multiobjective problems, low-dimensional and high-dimensional problems, unimodal and multimodal problems, and discrete and continuous problems.



The book is organized as follows:

- Chapter 1 introduces the development of intelligent optimization algorithms and their advantages and disadvantages.

- Chapter 2 introduces the process and basic principles of the atom search optimization algorithm and tests the algorithm.

- Chapter 3 applies the atom search optimization algorithm in engineering issues.

- Chapter 4 introduces the proposed process and basic principle of the artificial ecosystem-based optimization algorithm and tests the algorithm.

- Chapter 5 employs the artificial ecosystem-based optimization algorithm in engineering applications.

- Chapter 6 presents the inspiration and description of the supply-demand-based optimization.

- Chapter 7 demonstrates the applications of the supply-demand-based optimization in different engineering cases.

- Appendix A lists mathematical benchmark functions used to test the performance of various algorithms.

- Appendix B lists optimization formulations of various engineering problems.

- Appendix C provides the MATLAB codes of three algorithms.



We have been working on intelligent optimization algorithms for many years and have made some achievements. The book is compiled on the basis of the results of these achievements.


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