Maximizing Vector Distances for Purpose of Searching—A Study of Differential Evolution Suitability

Abstract

This paper explores suitability of using of differential evolution for maximizing of weighted distances of vectors in a set of vectors. Increase in vector distances simplifies searching for the best matching vector what is a common task in many areas (for instance in biometric identification of people). Maximizing of weighted distances itself is complex and nonlinear problem. The differential evolution is efficient enough and helps in decreasing of the computational complexity space compared to enumerative methods where all possible combinations are calculated. To find out, if differential evolution can help with the problem, model experiments were introduced and executed. Experiments showed that differential evolution is able to resolve the problem.