Название: Nature-inspired Optimization Algorithms and Soft Computing: Methods, technology and applications for IoTs, smart cities, healthcare and industrial automation
Автор: Rajeev Arya, Sangeeta Singh, Maheshwari P. Singh, Brijesh R. Iyer
Издательство: The Institution of Engineering and Technology
Год: 2023
Страниц: 298
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

We have witnessed an explosion of research activity around nature-inspired computing and bio-inspired optimization techniques, which can provide powerful tools for solving learning problems and data analysis in very large data sets. To design and implement optimization algorithms, several methods are used that bring superior performance. However, in some applications, the search space increases exponentially with the problem size. To overcome these limitations and to solve efficiently large scale combinatorial and highly nonlinear optimization problems, more flexible and adaptable algorithms are necessary.

Nature-inspired computing is oriented towards the application of outstanding information-processing aptitudes of the natural realm to the computational domain. The discipline of nature-inspired optimization algorithms is a major field of computational intelligence, soft computing and optimization. Metaheuristic search algorithms with population-based frameworks are capable of handling optimization in high-dimensional real-world problems for several domains including imaging, IoT, smart manufacturing, and healthcare. The integration of intelligence with smart technology enhances accuracy and efficiency. Smart devices and systems are revolutionizing the world by linking innovative thinking with innovative action and innovative implementation.

The aim of this edited book is to review the intertwining disciplines of nature-inspired computing and bio-inspired soft-computing (BISC) and their applications to real world challenges. The contributors cover the interaction between metaheuristics, such as evolutionary algorithms and swarm intelligence, with complex systems. They explain how to better handle different kinds of uncertainties in real-life problems using state-of-art of machine learning algorithms. They also explore future research perspectives to bridge the gap between theory and real-life day-to-day challenges for diverse domains of engineering.

The book will offer valuable insights to researchers and scientists from academia and industry in ICTs, IT and Computer Science, Data Science, AI and Machine Learning, swarm intelligence and complex systems. It is also a useful resource for professionals in related fields, and for advanced students with an interest in optimization and IoT applications.

Chapter 1 discusses various optimization techniques, search for optimality, need for optimization, brief history of metaheuristics optimization, difference between metaheuristics and heuristics optimization. It represents a confounded arrangement of prerequisites to understand the implications for metaheuristic optimization, theoretical analyses, and systematic approaches for the selection of optimization algorithms. It is difficult to choose an optimization algorithm that will find the best solution to the issue while considering all the limitations. There is not a single algorithm that can fix every optimization issue.

Chapter 2 represents an in-depth view of the nature-inspired optimization algorithms, search for an ideal algorithm, evolutionary algorithms, chemistry-based algorithms, physics-based algorithms, and various other algorithms. This chapter shows the path to general strategies and frameworks for efficiency improvement of nature-inspired algorithms, applications on single objective and multi objective problems and finally some concluding remarks.

Chapter 3 attempts to address the application aspects of nature-inspired optimization algorithms. It begins with the definition of nature-inspired optimization algorithms. In addition to that, it also deals with its implementation, and parameter tunning. Furthermore, it also presents a study on constrained and unconstrained optimization. Later, it covers the feature selection and practical engineering applications of nature-inspired optimization algorithms.
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Finally, Chapter 10 gives the detailed concluding remarks of the study present in the earlier chapters. It highlighted the various challenges and potentials of bio-inspired optimization algorithms for IoT applications, challenges, and opportunities of bio-inspired optimization algorithms for biomedical applications, recent trends in smarty cities planning based on nature-inspired computing, and lastly future perspectives of nature-inspired computing.




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