An improved memetic differential evolution for college students' comprehensive quality evaluation

Abstract

The evaluation of the comprehensive quality of college students is a key problem in the management of college student affairs. In this paper, we present an improved memetic differential evolution algorithm to get the best weights of the College Students' Comprehensive Quality Evaluation (CSCQE) problem. The proposed algorithm, called Uniform Memetic Differential Evolution (UMDE), hybridises differential evolution (DE) with a local search (LS) operator and a periodic uniform design re-initialisation scheme to balance the exploration and exploitation. UMDE is compared with five well-known evolutionary algorithms on twenty-one benchmark functions. The results show that UMDE can obtain results better than, or at least comparable with, the compared algorithms. And then, UMDE is used to solve the CSCQE problem. The results show that UMDE can find better weights of the index system.