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. 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.