Solving optimization problems has been the bane of scientific and combinatorial computing. Even though polynomial-time algorithms exist for some problems such as linear programming, matching, and network flow, most problems are intractable (or NP-complete). Some general-purpose algorithms such as integer programing and quadratic programming are available to make the handling of practical problems a bit easier. This book deals with the current trends in optimization algorithms. These algorithms are inspired by nature--many species seem to solve complex optimization problems efficiently. The book describes several different optimization algorithms.

This well-written book has 15 chapters and two appendices. The first chapter is a brief introduction to algorithms--most notably to traditional optimization algorithms. This chapter also gives a brief overview of the rest of the chapters.

The second chapter focuses on issues associated with the analysis of algorithms. Here, again, the emphasis is on the analysis of optimization algorithms. Parameter selection and parameter tuning (which form crucial aspects of optimization algorithms) are also discussed.

Chapters 3 to 11 nicely discuss different nature-inspired algorithms: random walks, simulated annealing, genetic algorithms, differential evolution, particle swarm optimization, the firefly algorithm, cuckoo search, bat algorithms, and flower pollination algorithms. Each nature-inspired algorithm is clearly and succinctly described in its own chapter. In each chapter, the algorithm, the meaning of parameters, and convergence criteria are discussed. Appendix B contains the MATLAB code for these algorithms. Examples are provided to drive home the concepts.

Chapters 12 to 14 provide a general framework for parameter tuning, how to deal with constraints, and how to solve multi-criteria objective functions. Again, these are very important considerations for optimization algorithms. Proper choices will make the algorithms more useful and practical. Chapter 15 contains other less developed nature-inspired algorithms (including ant algorithms, bee-inspired algorithms, harmony search, and hybrid algorithms).

Appendix A consists of a list of test function benchmarks used in the optimization algorithms. This will help researchers make comparisons based on Appendix A. Appendix B, as mentioned earlier, contains a list of MATLAB programs for different nature-inspired algorithms.

Since this book was written for a research-level audience, there are no exercises. However, it does have an extensive collection of references. Overall, it’s a very nice, impressive, and useful book for researchers working on optimization algorithms.