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Optimisation is one of the unavoidable key subjects in engineering and other real-world problems, which attracts researchers' and practitioners' attention for decades. On the other hand, computational algorithms nowadays play a definitive role in most real-life applications, from mobile phones to supercomputers, Internet servers, manufacturing, etc. An intelligent method for the enumeration of feasible solutions may lead to efficient computational algorithms. Swarm intelligence emerges as a rather new and novel of field computational intelligence that turned into a hot spot in optimization studies last two decades. This book brings together a number of research articles within the intersection of these two prominent subjects, which introduces techniques and approaches in detail and demonstrates how optimisation problems can be solved with heuristic and swarm intelligence approaches. It contains a few contributions on Particle Swarm Optimisation (PSO) area, which is one of renown swarm optimisation approaches that will shed light to issues around optimisation with swarm intelligence to guide junior researchers with implementation details provided.

Heuristic algorithms.

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This book introduces the theory and applications of metaheuristic algorithms. It also provides methods for solving practical problems such as software engineering problems, image recognition problems, problems in video networks, and problems in the ocean.

Heuristic algorithms.

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Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains. The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts.

Metaheuristics. --- Heuristic algorithms

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This book aims at attracting the interest of researchers and practitioners around the applicability of meta-heuristic algorithms to practical scenarios arising from different knowledge disciplines. Emphasis is placed on evolutionary algorithms and swarm intelligence as computational means to efficiently balance the tradeoff between optimality of the produced solutions and the complexity derived from their estimation. In summary, this book serves as a good start point for early-stage investigators in the initial steps of their research on meta-heuristics, grounded on both a thorough literature review and the practical orientation of its contents.

Metaheuristics. --- Heuristic algorithms --- Neural networks & fuzzy systems

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Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.

Metaheuristics. --- Heuristic algorithms --- Metaheuristics --- Métaheuristiques.