PSO Algorithm-Based Design of Intelligent Education Personalization System

Abstract

The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning.

Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

“…Four algorithms are selected for comparative study. They are BPSO [6,39], PSO [40,41], DE [29,42], and LSHADE-cnEpSin [11]. The reasons for selecting these algorithms are: ( 1 All the algorithms are executed on 100 learners, respectively.…”

“…The upper and lower bounds of the inertia weight are set according to [41]. Figure 1 shows the convergence curves of the tested algorithms for the first learner.…”

Learning Path Optimization Based on Multi-Attribute Matching and Variable Length Continuous Representation

“…Four algorithms are selected for comparative study. They are BPSO [9,46], PSO [47,48], DE [31,37], and LSHADE-cnEpSin [14]. The reasons for selecting these algorithms are: (1) BPSO is the binary version of the original Particle Swarm Optimization algorithm, which has proved effective over the Genetic Algorithm for e-course composition problems [9]; (2) PSO and DE are well-known optimizers for the continuous problem; (3) DE is the basic version of all the modified algorithms related to LSHADE-cnEpSin; (4) LSHADE-cnEpSin is the second winner in the competition of CEC 2017.…”

Retracted: PSO Algorithm‐Based Design of Intelligent Education Personalization System

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”