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.

• Chapter 1: Introduction to various optimization techniques
• Chapter 2: Nature-inspired optimization algorithm: an in-depth view
• Chapter 3: Application aspects of nature-inspired optimization algorithms
• Chapter 4: Particle swarm optimization applications and implications
• Chapter 5: Advanced optimization by nature-inspired algorithm
• Chapter 6: Application and challenges of optimization in Internet of Things (IoT)
• Chapter 7: Optimization applications and implications in biomedicines and healthcare
• Chapter 8: Applications and challenges of optimization in industrial automation
• Chapter 9: Expectations from modern evolutionary approaches for image processing
• Chapter 10: Conclusion