Practical real-life problems that occur in the design of electrical or mechanical systems, image processing, transportation, manufacturing, operations research, and so on can often be expressed as optimization problems. Many hard optimization problems are often solvable by a group of methods known commonly as metaheuristics. The focus of this book is on metaheuristics and some of its practical applications.

The book has 16 chapters. The prefatory chapter offers an introduction to metaheuristics with a focus on the principles of the most widely used metaheuristics and their extensions, and a short glimpse of their applications. According to the book’s introduction, “The simulated annealing method transposes the process of annealing to the solution of an optimization problem.” The chapter on simulated annealing brings out the analogy between an optimization problem and some physical phenomena. The applications of simulated annealing and its advantages and disadvantages are mentioned. The tabu search technique was validated, in 1986, by Glover. Its main feature is the use of mechanisms rooted in human memory. The tabu method uses memory, whereas simulated annealing does not utilize memory. The quadratic assignment problem is discussed. The focus is on basic tabu search and short-term and long-term memories. The variable neighborhood search metaheuristic has been developed since 1997. It has applications in many areas, including data mining, localization, communications, scheduling, vehicle routing, and graph theory. The method has many advantages. For example, it is easy to implement and it usually provides splendid solutions in a fair amount of time. As stated at the beginning of chapter 4, “The key concepts [related to the technique] are presented and illustrated by an example based upon a search for extremal graphs.” In the book, an iterative two-phase procedure known as the GRASP method is exemplified. This method, according to the chapter 5 introduction, “generates several configurations within the search space of a given problem, based on which it carries out an improvement phase.” The method has been applied to many hard combinatorial optimization problems, such as scheduling, quadratic assignment, the traveling salesman problem, and maintenance workforce scheduling. An application to the set covering problem is also described.

Evolutionary algorithms are search techniques founded on the biological evolution of species. Among evolutionary algorithms, genetic algorithms are widely known. Evolutionary methods were initially of little concern due to their substantial cost of execution. However, in recent years, they have experienced considerable growth, which may be attributed to the availability of more powerful computers, especially those exploiting parallelism.

The link between optimization and simulation of the behavior of ants was made in the 1990s. Since then, there have been several works of combinatorial optimization based on the utilization of a food source by ants. The ant colony optimization metaheuristic is discussed and compared to evolutionary algorithms. Genetic algorithms have been supported by biological principles such as selection, cross over, and mutation. However, recently, some methods have tried to exploit behaviors that have been good for the survival and development of biological populations. One such method is particle swarm optimization, which is based on cooperation without selection. The chapter on particle swarm optimization looks at the ingredients required for its operation and describes some of its versions. Several other metaheuristics, such as those based on artificial immune systems, bacterial foraging, and bio-geography, are also described in the book.

New algorithms for solving optimization problems have been inspired by nature. These are based on the behavior of bats, bees, fireflies, wasps, spiders, mosquitoes, glow worms, cuckoos, and so on. A chapter is devoted to the study of extensions of evolutionary algorithms to multi-modal and multi-objective optimization, while another one is dedicated to the “extension of evolutionary algorithms to constrained optimization.” The optimization of logistics systems using techniques based on metaheuristics and hybridization is also studied. Their application to supply chain management is explored. A chapter focuses entirely on the application of metaheuristics for solving vehicle routing problems. The last chapter concentrates on “applications to air traffic management,” especially air “route network optimization, airspace optimization, airport traffic optimization, and [aircraft] conflict resolution.”

The book’s chapters are written by prominent researchers in the metaheuristics field. They include adequate references, and many contain annotated bibliographies. It should be stated that metaheuristics is not without its critics. Several books and survey papers have been published on the subject. This book will be useful for academicians, problem solvers in industry, practitioners, students, and researchers.