Nature-inspired computing provides promising and effective approaches for problem solving in optimization, machine intelligence, data mining and resource management. Nature has evolved over millions of years under a variety of challenging environments and can thus provide a rich source of inspiration for designing algorithms and approaches to tackle challenging problems in real-world applications.The success of these algorithms in applications has increased their popularity in recent years, and active research has also led to the significant increase in the number of algorithms in recent years. It is estimated that about 140 different types of algorithms now exist in the literature, and this number is certainly gradually increasing. Researchers have tried to find inspiration from various sources in nature, such as ants, bees, fish, birds, mammals, plants, physical and chemical systems such as gravity, river systems, waves and pheromone. This leads to a diverse of range of algorithms with different capabilities and different levels of performance.However, such diversity may also cause confusion and distractions from important research topics. For example, many researchers wonder why such algorithms work and what their mathematical foundations for different search algorithms are. At the moment, it still lacks good theoretical understanding of metaheuristics. In fact, without a good mathematical framework, it is difficult to establish any solid mathematical foundation for analysing such algorithms. Such lack of theoretical analysis, together with different claims of results, it is understandable that misunderstanding and criticism have arisen in the research community concerning some metaheuristic algorithmsThere is a strong need for the whole research community to review carefully the developments concerning metaheuristics and bio-inspired computation so as to identify the key challenges, to inspire further research and to encourage innovative approaches that can... The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals. From the content: The Nature of Nature: Why Nature Inspired Algorithms Work.- Improved Bat Algorithm in Noise-Free and Noisy Environments.- Multi-objective Ant Colony Optimisation in Wireless Sensor Networks.le
