Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems

This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature-inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities. This book: • Discusses load balancing and demand response using nature-inspired optimization techniques. • Presents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithms. • Covers disease diagnosis and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniques. • Explains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniques. • Illustrates medical image analysis and segmentation using Ant Colony optimization and Particle Swarm optimization techniques. Nature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.