Details for: Garg V. Role of Nature-Inspired Algorithms in Real-life Problems 2025

Garg V. Role of Nature-Inspired Algorithms in Real-life Problems 2025

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Textbook in PDF format The book includes nature-inspired optimization techniques and their applications. It offers recent trends in the field of nature-inspired algorithms for solving real-life problems in various fields related to manufacturing, Artificial Intelligence (AI), finance, etc. Nature-inspired optimization techniques are not only useful but also needed for solving open-ended problems. Understanding nature-inspired techniques and their importance for solving real-life problems can open many directions for researchers and academicians. This book will be helpful in acquiring knowledge of nature-inspired optimization techniques in various fields of real-life applications. In an era where technological advancements drive the competitive edge across industries, the application of nature-inspired algorithms stands at the forefront of solving complex, real-world problems. These algorithms, inspired by natural processes such as evolution, swarming, and biogeography, offer innovative and efficient solutions that traditional methods often fail to achieve. This book serves as a comprehensive guide to understanding and applying these powerful techniques to a diverse array of practical challenges. The journey of exploring nature-inspired algorithms began with a simple yet profound question: how can the principles observed in nature be harnessed to solve intricate human problems? The answers lie in the development and application of algorithms that mimic natural phenomena, such as genetic algorithms based on natural selection, particle swarm optimization inspired by the social behavior of birds, and biogeography-based optimization reflecting the migration patterns of species. These algorithms have demonstrated remarkable success in areas ranging from engineering design and financial modeling to healthcare and environmental management. This book is structured to provide both theoretical foundations and practical applications of nature-inspired algorithms. The aim is to equip researchers, practitioners, and students with the knowledge and tools necessary to implement these algorithms effectively in their respective fields. Swarm optimization is a powerful optimization technique inspired by the collective behaviour of social organisms, such as bird flocks, ant colonies, and fish schools. This abstract provides a brief overview of the fundamentals of swarm optimization, highlighting its key principles and applications. Swarm optimization algorithms are a class of population-based metaheuristic algorithms that simulate the collaboration and interaction of individuals within a group to find optimal solutions to complex problems. The fundamental concept is rooted in the idea that collective intelligence and cooperation among individuals in a swarm can lead to efficient and effective problem-solving. The basic components of a swarm optimization algorithm include a population of individuals (particles or agents), a fitness function to evaluate the quality of solutions, and mechanisms for updating the positions or behaviours of individuals based on their own experience and the collective information within the swarm. The most well-known swarm optimization algorithm is Particle Swarm Optimization (PSO), where particles in the swarm adjust their positions in the solution space based on their personal best and the global best solutions found so far. PSO is characterized by its simplicity, easy implementation, and ability to handle complex, multi-dimensional optimization problems. Other swarm optimization algorithms include Ant Colony Optimization (ACO), inspired by the foraging behaviour of ants, and Bee Algorithm, inspired by the foraging behaviour of honey bees. These algorithms often exhibit robustness, adaptability, and parallelism, making them suitable for a wide range of optimization tasks. Swarm optimization has found applications in various fields, including engineering design, image processing, data clustering, robotics, finance, and telecommunications. Its success is attributed to its ability to efficiently explore solution spaces, handle high-dimensional problems, and converge to near-optimal solutions. Swarm optimization leverages the principles of collective intelligence from nature to address complex optimization problems. Its adaptability and versatility make it a valuable tool in the optimization toolbox, with numerous applications across different domains. In Chap. 1, a survey of intelligent system used for sign language recognition system is given. In Chap. 2, a newly proposed Sine Cosine algorithm is used for multi-objective dynamic economic emission dispatch problems. Chapter 3 focuses on improved binary algorithm for wireless sensor networks. In Chap. 4, a review on usage of nature-inspired algorithms for optimization in renewable energy is given. Chapter 5 gives detailed study of particle swarm optimization for cost optimization for single server double orbit retrial queue. In Chap. 6, an improved version of marine predator algorithm is proposed for real-world problems in engineering. The inspiration for this book comes from a growing need to bridge the gap between theoretical research and practical implementation. While significant progress has been made in developing sophisticated algorithms, their potential remains largely untapped in solving real-world problems. Through this book, we aim to illuminate the path from theory to practice, providing insights into the practical considerations and challenges encountered in real-world applications. Preface A Deep Survey of Intelligent Systems for Sign Language Recognition System Effective Multi-objective Dynamic Economic Emission Dispatch Optimization with Electric Vehicles Using Hybrid Oppositional-Based Learning Sine Cosine Algorithm Wireless Sensor Networks Lifetime Optimisation Using Gaussian Integrated Bat Algorithm Cost Optimization and ANFIS Computing for Single Server Double Orbit Retrial Queue Using PSO Fundamental of Swarm Optimization Improved Marine Predator Algorithm for Real World Problems

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1. Garg V. Role of Nature-Inspired Algorithms in Real-life Problems 2025.pdf13.18 MiB