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The purpose of designing this book is to portray certain practical applications of nature-inspired computation in machine learning for the better understanding of the world around us. The focus is to portray and present recent developments in the areas where nature- inspired algorithms are specifically designed and applied to solve complex real-world problems in data analytics and pattern recognition, by means of domain-specific solutions. Various nature-inspired algorithms and their multidisciplinary applications (in mechanical engineering, electrical engineering, machine learning, image processing, data mining and wireless network domains are detailed, which will make this book a handy reference guide.

Nature-inspired algorithms.

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"Moth-Flame Optimization algorithm is an emerging meta-heuristic and has been widely used in both science and industry. Solving optimization problem using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, noisy parameters, just to name a few. Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides an in-depth analysis of this algorithm and the existing methods in the literature to cope with such challenges. Key features: Reviews the literature of the Moth-Flame Optimization algorithm. Provides an in-depth analysis of equations, mathematical models, and mechanisms of the Moth-Flame Optimization algorithm. Proposes different variants of the Moth-Flame Optimization algorithm to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems. Demonstrates how to design, develop, and test different hybrids of Moth-Flame Optimization algorithm. Introduces several applications areas of the Moth-Flame Optimization algorithm. This handbook will interest researchers in evolutionary computation, meta-heuristics and to those who are interested in applying Moth-Flame Optimization algorithm and swarm intelligence methods overall to different application areas"--

Nature-inspired algorithms. --- Artificial intelligence.

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"The text discusses nature inspired algorithms and their applications in a comprehensive manner. It will be an ideal reference text for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering"--

Nature-inspired algorithms. --- Engineering --- Data processing.

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Gain insight into the world of nature-inspired optimization techniques and algorithms. This book will prepare you to apply different nature-inspired optimization techniques to solve problems using Java. You'll start with an introduction to the hidden algorithms that nature uses and find the approximate solutions to optimization problems. You'll then see how living creatures such as fish and birds are able to perform computation to solve specific optimization tasks. This book also covers various nature-inspired algorithms by reviewing code examples for each one followed by crisp and clear explanations of the algorithm using Java code. You'll examine the use of each algorithm in specific industry scenarios such as fleet scheduling in supply chain management, and shop floor management in industrial and manufacturing applications. Nature-Inspired Optimization Algorithms with Java is your pathway to understanding a variety of optimization problems that exist in various industries and domains and it will develop an ability to apply nature-inspired algorithms to find approximate solutions to them. You will: Study optimization and its problems Examine nature-inspired algorithms such as Particle Swarm, Gray wolf, etc. See how nature-inspired algorithms are being used to solve optimization problems Use Java for solving the different nature-inspired algorithms with real-world examples.

Programming --- Computer architecture. Operating systems --- Open Source --- Java (informatica) --- Mathematical optimization. --- Nature-inspired algorithms. --- Java (Computer program language)

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Mathematical optimization. --- Nature-inspired algorithms. --- Algorithms --- Natural computation --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis