A Multi-objective PSO with Pareto Archive for Personalized E-Course Composition in Moodle Learning System

A velocity-free fully informed particle swarm optimization algorithm is firstly proposed for multi-objective optimization problems in this paper. It finds the non-dominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. Distinc...

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Bibliographic Details
Published in2015 8th International Symposium on Computational Intelligence and Design (ISCID) Vol. 2; pp. 21 - 24
Main Authors Ying Gao, Lingxi Peng, Fufang Li, MiaoLiu, Waixi Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Algorithm design and analysis
E-learning
Electronic learning
Learning systems
Moodle
Multi-objective optimization
Pareto dominance
Pareto optimization
PSO
Sociology
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Summary:A velocity-free fully informed particle swarm optimization algorithm is firstly proposed for multi-objective optimization problems in this paper. It finds the non-dominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. Distinct from other multi-objective PSO, particles in swarm only have position without velocity and all personal best positions are considered to update particle position in the algorithm. The theoretical analysis implies that the algorithm will cause the swarm mean converge to the center of the Pareto optimal solution set in a multi-objective search space. Then, the algorithm is applied to the personalized e-course composition in Moodle learning system. The relative experimental results show that the algorithm has better performance and is effective.
ISBN:9781467395861
1467395862
DOI:10.1109/ISCID.2015.27