1556-603X/06/$20.00©2006IEEE FEBRUARY 2006 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 43 Carlos Brizuela CICESE Research Center, MEXICO Book Review S olving real-world problems involves conflicting objectives; this implies that an ideal point that is optimum under all objectives at the same time does not exist. The definition of what the optimum is and how to find it, under these conflicting objectives, has led to the development of multiobjec- tive (MO) optimization [1-2]. The edi- tors, who are well-recognized researchers in the design of MOEAs, have done an excellent work in compil- ing a representative set of applications that is clearly state-of-the-art. The authors of each chapter contribute their point of view about how to apply MOEAs to solve specific problems, which enriches the technical content of the book. The book is about applica- tions that resist being solved by tradi- tional methods. The book is conveniently organized in Chapter 1, and the remaining 29 chap- ters are divided into four parts. Chapter 1 gives a brief introduction to the field of MOEA and sets up the basic concepts needed to understand the rest of the book, along with a brief description of each chapter. Applications in engineering are dealt with in Chapters 2–13 (Part I). Scientific applications are the concern in Chapters 14–19 (Part II). Chapters 20–24 (Part III) deal with industrial applications. Chapters 25–30 (Part IV) deal with mis- cellaneous applications. Each chapter, in these parts, is organized as follows. Part I: Engineering Applications Chapter 2 introduces applications in engineering design such as welded beam, bulk carrier and shape optimization problems. The proposed MOEA handles the constraint that considers nondomi- nance, which reduces the computational effort of nondominant sorting. The design of industrial electromag- netic devices is dealt with in Chapter 3. Here, the main concern is the design of an efficient MOEA since the objective function evaluation is very expensive. A nondominated-sorting evolutionary strategy is proposed to achieve this goal. Chapter 4 deals with the monitoring design problem for groundwater. The NSGA-II, along with a variant, are pro- posed to tackle this problem. An introduction to the design of combina- torial-logic circuits is presented in Chapter 5. A particle swarm with an MO selection strat- egy is introduced to solve the problem. Autonomous vehi- cles navigation is the application presented in Chapter 6. Specifi- cally, the problem of sensor and vehicle parameter estimation is solved by means of a gradient-based method and the multiobjective continu- ous evolutionary algorithm. Chapter 7 deals with the design of control systems for linear time-invariant, non-minimal phase plants. The approach is based upon a toolbox of previously designed MOEAs. The theme for Chapter 8 has to do with the resolution of polymer extru- sion problems. The model for them is the well-known Traveling Salesman Problem. A reduced Pareto set GA with elitism is proposed as the methodology to solve the problem. Chapter 9 presents the Inverted and Shrinkable Pareto Archived Evolutionary Strategy (S-PAES) for three truss opti- mization problems. The algorithm is an extension of PAES for single-objective optimization. City and region planning problem is the application considered in Chapter 10. A real-world case is presented as an example of the application. The model- ing of the planning as an MO problem is described. Interesting aspects regarding the applicability of the nondominated solutions are also discussed. Chapter 11 presents a bi-objective covering tour problem. Specific opera- tors are designed to improve the effi- ciency of the MOEA; the quality of solutions generated by this method is compared with solutions generat- ed by an exact method. This is an interesting problem since it is usu- ally modeled as a sin- gle-objective problem with a restriction. However, this restric- tion is actually the sec- ond objective function. An interesting and well-designed computer engineering benchmark for multi-objective optimiz- ers is proposed in Chapter 12. The benchmark is related to the Packet Processor Design, a processor that can be found in network routers and switches, and its main function is net- work packing processing. Many MOEAs are tested on the proposed benchmark. Chapter 13 deals with aerodynamic wing optimization by means of the Adaptive Range Multiobjective Genetic Algorithm. This algorithm is an exten- sion of the standard MOGA to deal with adaptive ranges. The problem involves four objectives and 72 design variables, which makes it a very chal- lenging problem to solve. Applications of Multi-Objective Evolutionary Algorithms, Vol. 1, by Carlos A. Coello Coello and Gary B. Lamont, World Scientific, 2004, 761 pp., Hardcover, ISBN: 981-256- 106-4.