Abstract
Information literacy assessment is extremely important for the evaluation of the information literacy skills of college students. Intelligent optimization technique is an effective strategy to optimize the weight parameters of the information literacy assessment index system (ILAIS). In this paper, a new version of differential evolution algorithm (DE), named hybrid differential evolution with model-based reinitialization (HDEMR), is proposed to accurately fit the weight parameters of ILAIS. The main contributions of this paper are as follows: firstly, an improved contraction criterion which is based on the population entropy in objective space and the maximum distance in decision space is employed to decide when the local search starts. Secondly, a modified model-based population reinitialization strategy is designed to enhance the global search ability of HDEMR to handle complex problems. Two types of experiments are designed to assess the performance of HDEMR. In the first type of experiments, HDEMR is tested and compared with seven well-known DE variants on CEC2005 and CEC2014 benchmark functions. In the second type of experiments, HDEMR is compared with the well-known and widely used deterministic algorithm DIRECT on GKLS test classes. The experimental results demonstrate the effectiveness of HDEMR for global numerical optimization and show better performance. Furthermore, HDEMR is applied to optimize the weight parameters of ILAIS at China University of Geosciences (CUG), and satisfactory results are obtained.
Highlights
With the arrival of the new economic era based on information and knowledge, information has become an important factor in all fields of society
It can be seen that hybrid differential evolution with model-based reinitialization (HDEMR) performs significantly better than HDE on 8 test functions
Among the 30 test functions, HDEMR is significantly better than JADE, CoDE, jDE, MPEDE, SHADE, LSHADE, and LSHADE-ε in 16, 13, 19, 23, 12, 9, and 7 functions, respectively, while the number of functions that JADE, CoDE, jDE, MPEDE, SHADE, LSHADE, and LSHADE-ε performs significantly better are 4, 10, 3, 2, 7, 15, and 11
Summary
With the arrival of the new economic era based on information and knowledge, information has become an important factor in all fields of society. In the heuristic search methods, DE and its variants have emerged as one of the most competitive and versatile families of evolutionary algorithms which belong to computational intelligence It was first proposed by Storn and Price [20] to solve global numerical optimization problems over continuous search spaces. Peng et al [43] proposed an improved memetic differential evolution algorithm, called MDE, which hybridized differential evolution with a local search (LS) operator and periodic reinitialization to balance exploration and exploitation for solving global optimization problems. E paper is organized as follows: in Section 2, the college students’ information literacy assessment index system and the weight parameter optimization model of ILAIS are introduced.
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