Swarm Intelligence and Bio-Inspired Computation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Swarm Intelligence and Bio-Inspired Computation write by Xin-She Yang. This book was released on 2013-05-16. Swarm Intelligence and Bio-Inspired Computation available in PDF, EPUB and Kindle. Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.
Swarm Intelligence and Bio-Inspired Computation
Swarm Intelligence and Bio-Inspired Computation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Swarm Intelligence and Bio-Inspired Computation write by Xin-She Yang. This book was released on 2013-05-16. Swarm Intelligence and Bio-Inspired Computation available in PDF, EPUB and Kindle. Swarm intelligence (SI) and bio-inspired computing in general have attracted great interest in almost every area of science, engineering, and industry over the last two decades. In this chapter, we provide an overview of some of the most widely used bio-inspired algorithms, especially those based on SI such as cuckoo search, firefly algorithm, and particle swarm optimization. We also analyze the essence of algorithms and their connections to self-organization. Furthermore, we highlight the main challenging issues associated with these metaheuristic algorithms with in-depth discussions. Finally, we provide some key, open problems that need to be addressed in the next decade.
Swarm Intelligence and Bio-Inspired Computation
Swarm Intelligence and Bio-Inspired Computation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Swarm Intelligence and Bio-Inspired Computation write by Priti Srinivas Sajja. This book was released on 2013-05-16. Swarm Intelligence and Bio-Inspired Computation available in PDF, EPUB and Kindle. Bio-inspired models have taken inspiration from the nature to solve challenging problems in an intelligent manner. Major aims of such bio-inspired models of computation are to propose new unconventional computing architectures and novel problem solving paradigms. Computing models such as artificial neural network (ANN), genetic algorithm (GA), and swarm intelligence (SI) are major constituent models of the bio-inspired approach. Applications of these models are ubiquitous and hence proposed to be applied for Semantic Web. The chapter discusses fundamentals of these bio-inspired constituents along with some heuristic that can be used to design and implement these constituents and briefly surveys recent applications of these models for the Semantic Web. The study shows that the objective of the Semantic Web is better met with such approach and the Web can be accessed in more human-oriented way. At the end, a generic framework for web content filtering based on neuro-fuzzy approach is presented. By considering online webpages and fuzzy user profile, the proposed system classifies the webpages into vague categories using a neural network.
Bio-Inspired Computation in Telecommunications
Bio-Inspired Computation in Telecommunications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bio-Inspired Computation in Telecommunications write by Xin-She Yang. This book was released on 2015-02-11. Bio-Inspired Computation in Telecommunications available in PDF, EPUB and Kindle. Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.
Swarm Intelligence and Bio-Inspired Computation
Swarm Intelligence and Bio-Inspired Computation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Swarm Intelligence and Bio-Inspired Computation write by Raha Imanirad. This book was released on 2013-05-16. Swarm Intelligence and Bio-Inspired Computation available in PDF, EPUB and Kindle. In solving many practical mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different approaches to the particular problem. This is because decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult—if not impossible—to quantify and capture at the time that the supporting decision models are constructed. There are invariably unmodeled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modeled objective(s) but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modeling-to-generate-alternatives (MGA). This chapter provides a synopsis of various MGA techniques and demonstrates how biologically inspired MGA algorithms are particularly efficient at creating multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. The efficacy and efficiency of these MGA methods are demonstrated using a number of case studies.
