A comprehensive survey on multi-objective evolutionary optimization in power system applications

Abstract

Many technical areas in power systems require the simultaneous optimization of multiple and often conflicting objective functions with complicated non-linear constraints. The recent studies on multi-objective evolutionary computation methods have shown that the population-based stochastic algorithms are the most attractive approaches for this class of problems. Moreover, these methods can be efficiently used to eliminate most of the difficulties of classical single-objective methods such as the sensitivity to the shape of the Pareto-optimal front and the necessity of multiple runs to find set of Pareto-optimal solutions. At first, in this paper, the concept related to multi-objective optimization and Pareto-optimality is presented. Further, this paper provides a comprehensive survey on applications of the newly developed Pareto-based multi-objective evolutionary computation methods for solving real-world power system multiobjective nonlinear optimization problems.