Constrained Multi-Objective Optimization Using Differential Evolution

Most real-world optimization problems include multiple objective functions and several constraints. The Differential Evolution algorithm was initially developed for unconstrained single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multi-objective optimization recently. In this work a multi-objective Differential Evolution algorithm is extended for handling constraints. Results from three test functions confirm that the extended algorithm is capable of successfully optimizing constrained problems. Four implementations are compared that differ in the selection scheme and in the assignment of the crowding distance.