Description

New Releases,Mathematics,Computer,Statistics

This book complements our previous book on Optimization Techniques, wherein readers are acquainted with various optimization methods rooted in theoretical concepts, aimed at solving real-world optimization challenges. Unlike the previous book, this one emphasizes computational techniques that do not rely on any particular mathematical optimization theory. Nature-Inspired Optimization Techniques have the capability to effectively tackle both conventional and practical real-world optimization problems without the need of prior knowledge about the problem’s context. These techniques do not demand the fulfilment of continuity and differentiability conditions of the objective function or constraints. Instead of starting with a single initial estimate, these techniques begin with a randomly generated set of feasible solutions, which are then progressively refined using specially crafted operators. They are more inclined to reach the global optimal solution(s) rather than settling for local optimal solution(s). Moreover, they are simple to implement and are integrated into many popular software packages.

These techniques can be broadly divided into four categories. The first category includes Evolutionary Algorithms, which are rooted in Darwin’s Theory of evolution, particularly focusing on competition and the survival of the fittest. Genetic Algorithms and Differential Evolution are two popular methods in this category. The second category comprises techniques based on swarm behaviour which rely on the foraging behaviour of swarms, where swarm particles move through the search space and continuously update their velocities and positions. Particle Swarm Optimization and Artificial Bee Colony inspired by the self-organization and division of labour seen in bees, are examples in this category. Third category includes techniques based on physical laws of nature, such as the Gravitational Search Algorithm, which simulates the gravitational interaction between masses according to Newton’s laws of gravitation and motion. Similarly, the Harmony Search Algorithm is inspired by the laws of music. A fourth category is based on other natural phenomena like biogeography-based optimization deriving its inspiration from concept of human immigration-emigration, etc.

This introductory book forms a foundation for readers in the areas of Mathematics, Statistics, Artificial Intelligence, Machine Learning, Computer Science and Engineering, Finance and Business. It will be useful for undergraduate, postgraduate and research scholars for understanding the concepts as well as its implementation to solve their particular problem at hand. Executives from corporate world can also use it to solve their domain specific optimization problems. After each chapter, this book includes a series of short-answer review questions, with answers provided within the text of the chapters themselves. Additionally, to facilitate hands-on practice, several assignments are proposed at the end of each chapter. The final chapter of the book demonstrates how to design a new nature-inspired optimization technique to address a specific problem in cases where existing techniques prove inadequate.