ABSTRACT
This chapter discusses a comparative analysis between performing single-function and multiple-function optimization processes. It shows some of the traditional methods used to solve multi-objective optimization problems and also shows some metaheuristics to solve multi-objective optimization processes. There are several methods that can be used to solve multi-objective problems using single-objective approximations. Some of them are weighted sum, ε-constraint, weighted metrics, Benson, lexicographic, and min-max, among others. Referent Objective Method, like the weighted sum method, allows one to transform a multi-objective optimization problem into a single-objective problem. Traditionally, metaheuristics are good techniques to solve optimization problems in which to accomplish convergence toward optimum one must perform combinatorial analysis of solutions. An evolutionary algorithm consists of the individuals, the population of such individuals, and the fitness of each of the individuals and of the genetic operators. In the case of evolutionary algorithms, the Multi-Objective Evolutionary Algorithm is an adaptation to solve multi-objective problems.
