ABSTRACT
This chapter presents the main features of Multiobjective Optimization (MO) starting from the classical approaches to state-of-the-art methodologies, all of which can be used for practical designs. The popular optimization methods for solving MO problems are generally classified into three categories: enumerative methods, deterministic methods and stochastic methods. Evolutionary computation in multiobjective optimization (MO) has become a popular technique for solving problems that are considered to be conflicting, constrained, and sometimes mathematically intangible. Three common evolutionary algorithms are evolutionary programming, evolution strategies and genetic algorithms. Multiobjective Genetic Algorithm was proposed by Fonseca and Fleming. It has three features: a modified version of Goldberg’s ranking scheme, modified fitness assignment, and niche count. The technique of fitness sharing was suggested to avoid such a problem. It utilizes individual competition for finite resources in a closed environment. The chapter describes computational procedures, organization flow charts, and the necessary equations of each scheme.
