Preface

Nature-inspired optimization algorithms have become increasingly popular in recent years, and most of these metaheuristic algorithms, such as particle swarm opti-mization and firefly algorithms, are often based on swarm intelligence. Swarm-intelligence-based algorithms such as cuckoo search and firefly algorithms have been found to be very efficient.

The literature has expanded significantly in the last 10 years, intensifying the need to review and summarize these optimization algorithms. Therefore, this book strives to introduce the latest developments regarding all major nature-inspired algorithms, including ant and bee algorithms, bat algorithms, cuckoo search, firefly algorithms, flower algorithms, genetic algorithms, differential evolution, harmony search, simu-lated annealing, particle swarm optimization, and others. We also discuss hybrid methods, multiobjective optimization, and the ways of dealing with constraints.

Organization of the book's contents follows a logical order so that we can introduce these algorithms for optimization in a natural way. As a result, we do not follow the order of historical developments. We group algorithms and analyze them in terms of common criteria and similarities to help readers gain better insight into these algorithms.

This book's emphasis is on the introduction of basic algorithms, analysis of key components of these algorithms, and some key steps in implementation. However, we do not focus too much on the exact implementation using programming languages, though we do provide some demo codes in the Appendices.

The diversity and popularity of nature-inspired algorithms do not mean there is no problem that needs urgent attention. In fact, there are many important questions that remain open problems. For example, there are some significant gaps between theory and practice. On one hand, nature-inspired algorithms for optimization are very suc-cessful and can obtain optimal solutions in a reasonably practical time. On the other hand, mathematical analysis of key aspects of these algorithms, such as convergence, balance of solution accuracy and computational efforts, is lacking, as is the tuning and control of parameters.

Nature has evolved over billions of years, providing a rich source of inspiration. Researchers have drawn various inspirations to develop a diverse range of algorithms with different degrees of success. Such diversity and success do not mean that we should focus on developing more algorithms for the sake of algorithm developments, or even worse, for the sake of publication. We do not encourage readers to develop new algorithms such as grass, tree, tiger, penguin, snow, sky, ocean, or Hobbit algorithms.

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These new algorithms may only provide distractions from the solution of really challenging and truly important problems in optimization. New algorithms may be developed only if they provide truly novel ideas and really efficient techniques to solve challenging problems that are not solved by existing algorithms and methods.

It is highly desirable that readers gain some insight into the nature of different nature-inspired algorithms and can thus take on the challenges to solve key problems that need to be solved. These challenges include the mathematical proof of conver-gence of some bio-inspired algorithms, the theoretical framework of parameter tuning and control; statistical measures of performance comparison; solution of large-scale, real-world applications; and real progress on tackling nondeterministic polynomial (NP)-hard problems. Solving these challenging problems is becoming more important than ever before.

It can be expected that highly efficient, truly intelligent, self-adaptive, and self-evolving algorithms may emerge in the not-so-distant future so that challenging prob-lems of crucial importance (e.g., the traveling salesman problem and protein structure prediction) can be solved more efficiently.

Any insight gained or any efficient tools developed will no doubt have a huge impact on the ways that we solve tough problems in optimization, computational intelligence, and engineering design applications.

Xin-She YangLondon, 2013