This book presents recent developments in a specific area of computational intelligence, namely fuzzy logic augmentation of nature-inspired optimization metaheuristics and their applications. It has two parts, each one consisting of a number of papers grouped around a common topic.

The first part’s seven papers deal mainly with theoretical aspects. They describe new, improved metaheuristic algorithms using fuzzy systems. Some of these algorithms are improved ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms using fuzzy logic for dynamic parameter tuning; a new bat algorithm with fuzzy logic for dynamical parameter adaptation; a firefly algorithm applied for optimizing benchmark functions; a harmony search algorithm; and a cuckoo search algorithm.

The second part consists of five papers that use nature-inspired optimization methods for solving complex optimization problems encountered in various applications. Some of the proposed algorithms are a gravitational search algorithm for recognition of medical images, a PSO algorithm with type-1 and type-2 fuzzy integration for time series prediction, and a new hybrid method obtained by combining PSO and genetic algorithms using fuzzy logic applied to mathematical function optimization.

In my opinion, the book is very interesting for those working in the field of computational intelligence. It will be useful for the researchers and practitioners working in this area of research.