A metaheuristic is a procedure or heuristic used to discover, yield, or choose a heuristic that may furnish a decent solution to an optimization problem.

This book on recent developments in metaheuristics has been published as volume 62 of the “Operations Research/Computer Science Interfaces” series by Springer. It has 28 chapters. These chapters may be classified into two parts. The first part, composed of the first ten chapters, presents new optimization and modeling techniques based on metaheuristics. The second part, making up the rest of the book, develops advanced approaches to solve real-life problems such as scheduling, vehicle routing, multimedia sensor networks, supplier selection, bin packing, object tracking, and radio frequency identification (RFID). All the chapters in the book are research works that were presented at two conferences held in Morocco during the years 2014 and 2015.

The first chapter looks at the adaptive particle swarm optimization technique. The focus is on constructing hidden Markov model classifiers for that approach. Multiobjective optimization is an important optimization technique. So it is studied when there is uncertainty, and for this a probabilistic framework is proposed. Neighborhoods may be combined, and this is helpful in local search. Tabu search is an important optimization technique that is given attention. The study concentrates on its application for managing production. A chapter looks at hyperheuristics and reports extrapolated hyperheuristics that can integrate discretionary domain knowledge. A metaheuristic-based solver language is depicted.

Hill climbing is another well-studied optimization technique that is discussed. Genetic algorithms have been widely used for the solution of classical problems of graph theory. A chapter looks at their design. Algorithms for process design are elaborated. The cuckoo search algorithm is applied for solving a manufacturing problem. Hybrids of branch-and-bound algorithms are considered for wireless multimedia senor networks. A variant of the multicriteria decision-making approach is used for the selection of suppliers. Multiobjective optimization techniques are employed for restructuring traffic networks. Air traffic management problems can be attacked using hybrid metaheuristics. A variant of the covering tour problem is examined, and genetic algorithms are applied in this context.

Dynamic network flow problems are a fertile ground for applying metaheuristics. A variant of the widely considered vehicle routing problem known as the surveillance vehicle routing problem is looked at. For this, greedy randomized adaptive search methods are advised. The applications of strip algorithms are detailed. The temporal bin-packing problem is discussed, and metaheuristics for this are suggested. A problem known as the dynamic technician routing and scheduling problem is discussed. Algorithms for optimizing air route connections are offered. The application of evolutionary algorithms for the solution of problems is indicated. Iterated local search is another search technique that is concentrated on. Fuzzy job shop problems and multiobjective evolutionary algorithms for solving them are presented. A re-identification method using metaheuristics is covered. The feature selection problem is tackled using a binary particle swarm optimization and gravitational search algorithm approach. Algorithms are pointed out for optimal deployment of readers for RFID networks. Bat algorithms are viewed for the analysis of security audit trails.

The chapters in the book contain adequate references to the literature for further study. The index is also quite helpful. The book sheds light on recent developments in the field of metaheuristics with emphases on algorithms, applications, thought-provoking questions, theory, and implementation aspects. The contributors extend advanced applications of metaheuristics that have a potential of broadening research fields. This book will be useful for researchers and practitioners in operations research and computer science. It will also be useful for offering an advanced course on metaheuristics and can definitely be used as a starting point for research by students pursuing doctoral or master’s degree programs. I recommend this book for the aforementioned categories of readers.