Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. The first book presents the use of classical methods and preference-based techniques. The book explains classical methods for solving MOO problems through nine chapters. Topics covered in this part are the design of current MOO problems, the complexity of MOO problems with nonlinearities and uncertainties, the theory of Pareto optimality, the introductory problem solving methods (including Zeleny's simplex method), preference-based methods, structures of MOO problems (such as the mixed-integer programming, hierarchical optimization, fuzzy logic programming and bimatrix games). Multi-Objective Optimization in Theory and Practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.