A variant of differential evolution for discrete optimization problems requiring mutually distinct parameters

Abstract

A large number of real world problems are formulated in terms of a set of discrete or integer variables for which an optimal set is obtained through appropriate optimization of a function. These problems are best represented using a set of discrete numbers over bounded or unbounded discrete spaces, in order to limit the search domain of the algorithm. In this work, Differential Evolution (DE) is used for the discrete prob- lem, where the search space is augmented to improve the performance of the technique. Although in principal DE is used to nd the optimal solution, the manner in which the space is stated and then searched is altered to improve the overall performance. Both unique and non-unique discrete sets of variables are investigated as control variables of the functions, and the algorithm for each is outlined accordingly. A number of established test functions are used to state the performance of the proposed DE discrete variable op- timization technique, when compared to real space DE optimization