Nature-Inspired Algorithms and Applications. Группа авторов
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–In Chapter 9, “Optimization Techniques for Removing Noise in Digital Medical Images,” Dr. D. Devasena, Dr. M. Jagadeeswari, Dr. B. Sharmila and Dr. K. Srinivasan introduce various types of evolutionary computation algorithms inspired by biological, social and natural systems. These methods include the following algorithms: particle swarm optimization (PSO), bat algorithm (BA), firefly algorithm (FA), social spider optimization (SSO), collective animal behavior (CAB), differential evolution (DE), genetic algorithm (GA) and bacterial foraging algorithm (BFA). Thus, the evolutionary algorithms are ones that simulate biological, natural or social level systems to address real-time image processing problems.
–In Chapter 10, “Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis,” K. Hariprasath, Dr. S. Tamilselvi, Dr. N. M. Saravana Kumar, N. Kaviyavarshini and Dr. S. Balamurugan introduce many successful optimization approaches like swarm intelligence, machine intelligence, data mining and resource management. The swarm intelligence model is one of the popular computation theories that is motivated by common swarm frameworks. The three primary swarm protocols are to move in the same direction as its neighbors, to remain as close as possible to the neighbors, and to avoid collision among neighbors.
–In Chapter 11, “Applications of Cuckoo Search Algorithm for Optimization Problems,” Akanksha Deep and Prasant Kumar Dash introduce various optimization algorithms which are classified on the basis of two key elements—diversification and aggregation—generally known as exploitation and exploration. Exploration aims to find a contemporary solution which results in locating global optima, whereas exploitation aims to find local optima of the solution space explored.
–In Chapter 12, “Mapping of Real-World Problems to Nature-Inspired Algorithm Using Goal-Based Classification and TRIZ,” Palak Sukharamwala and Manojkumar Parmar present a novel method based on TRIZ to map real-world problems to nature problems. TRIZ is also known as the theory of inventive problem solving. Using the proposed framework, the best NIA can be identified to solve real-world problems. For this framework to work, a novel classification of the NIA based on the end goal that nature is trying to achieve is devised.
To conclude, we would like to extend our appreciation to our many colleagues. We also extend our sincere thanks to all the experts for providing preparatory comments on the book that will surely motivate the reader to read the topic. We also wish to thank the reviewers who took time to review this book, and are also very grateful to our family members for their patience, encouragement and understanding. Special thanks also go to many individuals at Scrivener Publishing, whose talents and efforts made the publication of this book possible. Finally, any suggestions or feedback from readers to improve the text will be highly appreciated.
The Editors September 2021
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Introduction to Nature-Inspired Computing
N.M. Saravana Kumar1*, K. Hariprasath2, N. Kaviyavarshini2 and A. Kavinya2
1Department of Artificial Intelligence and Data Science, M Kumarasamy College of Engineering, Karur, India
2Department of Information Technology, Vivekanandha College of Engineering for Women, Namakkal, India
Abstract
Nature-inspired algorithms have significance in solving many problems. This chapter provides an overview of nature-inspired algorithms like bio-inspired algorithm, swarm intelligence algorithm, and physical and chemical system–based algorithm. Many real-world problems are solved using nature-inspired algorithms and the role of optimization plays an important role. This chapter covers the basic working and classification of nature-inspired algorithms along with its area of applications. The purpose and its significance of each and every algorithm have been described. Also, the applications of algorithms comprise most of the real-time problems.
Keywords: Nature-inspired, bio-inspired, evolutionary computing, swarm intelligence, optimization, applications
1.1 Introduction
An algorithm is a finite series of definite procedure for finding significance of the pattern. They are utilized to explain a course of difficulties and then implement calculation. Algorithm are said to unambiguous and utilized for performing computation and dealing with other task.
Algorithm has different characteristics; they are unambiguous, well-defined input and output, determinate, realistic, and independent of language. Unambiguous refers to having only one interpretation which leads to only one conclusion. Well-defined input and output refers to defining the input and output clearly. Determinate refers to algorithm that must be finite as the algorithm should not conclude with infinite loop. Realistic refers to the algorithm that is general, simple, and practical which may be implemented with an accessible source. Independent of language refers to the algorithm that must be designed with independent of language that it can be implemented in any language.
The technique of optimization comprises nonlinear problem with huge variables containing design and more composite constraints in the application of real world. The problem of optimization is linked with decrease of cost, waste, and time or increase in performance, benefits, and profits. Optimization can be described as an attempt of generating solutions to a problem beneath bounded circumstances. Optimization techniques have arisen from a desire to utilize current resources inside the excellent possible way.
1.2 Aspiration From Nature
Always nature performs actions in an incredible approach. After the detectable phenomenon, the incalculable conspicuous effects at present are indiscernible. Theorists and experts have been penetrating this type of phenomenon in the centurial essence and making effort to grasp, recognize, accommodate, describe, and simulate the artificial structure. There are countless handler agents and extra energy that is present in both realistic and non-realistic world, nearly which are unfamiliar and hidden risk is beyond manhood apprehension in total. Those agents bear in collateral and usually in opposition to a very few other affording pattern and quality to nature and standardize the kinship, elegance, and agility of survival. This has to be noticed as the dialectical nature which prevails in the theory of the world progression. The expansion of risk in nature pursues a peculiar structure. In addition to this, also, intelligence dealing with the nature is implemented in a shared, self-formed, and optimum response without any fundamental domination.
This type of entire ordination, which is in various types—micro biological, physiologic, chemic, and sociality—is circulated as stated by the risk factor for low level to high level. This series formulate its common dependency and partnership with regard to mutual framework and its personal biography. The behavior retardation owing to the transformed conditions and these entire phenomenon best-known or little-known till now come up with an advanced concepts in science and various technologies, also computation which practice the procedures for resolving problems that is inspired by the nature additionally endeavor to comprehend the fundamental foundations and structures of nature that achieve complicated effort in an advantageous form with narrow assets and capableness. Science intermediates in-between the theorist and the world nature which was emerged before many years by developing advanced hypothesis, techniques, and implementation into well-known system of technological strive.
Manhood has been practicing to comprehend the nature of all time because of evolving advanced mechanisms as well as tools. Nature-inspired computing consists of several branches; one of them is integrative in nature that associates interpolating of knowledge together with information of