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Название: Nature-Inspired Algorithms and Applications
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Программы
isbn: 9781119681663
isbn:
Sonia Duggal
and
Seema Sharma
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-68174-8
Cover image: Pixabay.Com Cover design Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book was designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide. A brief description of the topics covered in each chapter is given below.
–In Chapter 1, “Introduction to Nature-Inspired Computing,” Dr. N. M. Saravana Kumar, K. Hariprasath, N. Kaviyavarshini and A. Kavinya introduce a new discipline that strives to develop new computing techniques through observing how naturally occurring phenomena behave to solve complex problems in environmental situations. Characterization of nature-inspired algorithms are also discussed.
–In Chapter 2, “Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning,” Dr. P. Mary Jeyanthi and Dr. A. Mansurali introduce various hybridized algorithms in the field of machine learning (ML) along with their applications. This chapter emphasizes the characteristics of a genetic algorithm (GA) which helps machine learning in GA’s consideration of genes (variables).
–In Chapter 3, “Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique,” Dr. K. Sasi Kala Rani and N. Pooranam address the challenges faced in tourism when a planned vacation to a specific destination is challenged by unforeseen events like adverse climate conditions that threaten to derail the trip. In this case, an optimal solution is generated by using heuristic value and an ACO algorithm in which the continuous orthogonal ant colony (COAC) method helps to solve real-world problems.
–In Chapter 4, “A Hybrid Bat-Genetic Algorithm-Based Novel Optimal Wavelet Filter for Compression of Image Data,” Renjith V. Ravi and Kamalraj Subramaniam explain how three modules, namely optimized transformation module, compression and encryption module and receiver module, are used. Initially, the input image is sub-band coded using hybrid bat-genetic algorithm-based optimized DWT. Subsequently, the encoding using SPIHT and chaos-based encryption is carried out. In receiver module, the received signal from the AWGN channel is demodulated, decrypted and de-compressed to obtain the estimated image. From the results, we can infer that the use of the proposed filter and technique has produced better image quality when compared to existing techniques.
–In Chapter 5, “A Swarm Robot for Harvesting a Paddy Field,” N. Pooranam and T. Vignesh discuss how the harvesting process can be improved in a positive way by using the PSO-based swarm intelligent algorithm to help in searching for and optimizing the process. The harvesting process has several steps: Reaping (cutting), threshing (separating process), and cleaning (removing non-grain material from grains). The PSO algorithm will find the positions of all robots to start harvesting and crust-based PSO will help to improve the optimization.
–In Chapter 6, “Firefly Algorithms,” Anupriya Jain, Seema Sharma and Sachin Sharma present the working principle of firefly algorithms (FA) in detail with the algorithm explained and its implementation ready for reference. In recent years, variants of FA to accommodate new problems have been introduced. The hybrid or modified models have tremendously improved the performance of a standard FA. These special cases and applications of this metaheuristic problem are discussed in detail.