Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generating k tests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.

[1]  Xiang Yu,et al.  Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems , 2017, PloS one.

[2]  Narayan Rangaraj,et al.  Exact approaches for static data segment allocation problem in an information network , 2015, Comput. Oper. Res..

[3]  Antonio Bolufé Röhler,et al.  Multi-swarm hybrid for multi-modal optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[4]  Mehmet Yildirim,et al.  A genetic algorithm for generating test from a question bank , 2009, Comput. Appl. Eng. Educ..

[5]  Zhile Yang,et al.  A novel hybrid teaching learning based multi-objective particle swarm optimization , 2017, Neurocomputing.

[6]  Mohammad Reza Banan,et al.  A new PSO-based algorithm for multi-objective optimization with continuous and discrete design variables , 2018 .

[7]  Roman Kuiava,et al.  A Procedure to Design Fault-Tolerant Wide-Area Damping Controllers , 2018, IEEE Access.

[8]  Songfeng Lu,et al.  A Particle Swarm Optimization Based on Multi Objective Functions with Uniform Design , 2016 .

[9]  W. Lewis.  Economic Development with Unlimited Supplies of Labour , 1954 .

[10]  Witold Pedrycz,et al.  Application of Particle Swarm Optimization to Create Multiple-Choice Tests , 2018, J. Inf. Sci. Eng..

[11]  Dazhi Pan,et al.  Multi-objective Optimization Based on Chaotic Particle Swarm Optimization , 2018 .

[12]  Thomas Bartz-Beielstein,et al.  Experimental Methods for the Analysis of Optimization Algorithms , 2010 .

[13]  K. K. Mishra,et al.  Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking , 2017, Applied Intelligence.

[14]  Swapnil Prakash Kapse,et al.  An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search , 2018, Int. J. Appl. Metaheuristic Comput..

[15]  Elsayed A. Sallam,et al.  Multi-swarm multi-objective optimization based on a hybrid strategy , 2017, Alexandria Engineering Journal.

[16]  Mahmoud Neji,et al.  A Hybrid Feature Selection for MRI Brain Tumor Classification , 2017, IBICA.

[17]  Sai Wang,et al.  A Hybrid Genetic Algorithm With Wrapper-Embedded Approaches for Feature Selection , 2018, IEEE Access.

[18]  Li Ma,et al.  Multi-objective Particle Swarm Optimization with Gradient Descent Search , 2014 .

[19]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[20]  D. Hunt.  Economic Theories of Development: An Analysis of Competing Paradigms , 1989 .

[21]  Licheng Jiao,et al.  A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization , 2017, Eur. J. Oper. Res..

[22]  Zhi-hui Zhan,et al.  A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting , 2018, Appl. Soft Comput..

[23]  Yen-Ting Lin,et al.  Dynamic question generation system for web-based testing using particle swarm optimization , 2009, Expert Syst. Appl..

[24]  Jiannong Cao,et al.  Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems , 2013, IEEE Transactions on Cybernetics.

[25]  Edgar Tello-Leal,et al.  R2-Based Multi/Many-Objective Particle Swarm Optimization , 2016, Comput. Intell. Neurosci..

[26]  Wei Jyh Heng,et al.  Question classification for e-learning by artificial neural network , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[27]  Daniel Kudenko,et al.  Tuning an Algorithm Using Design of Experiments , 2010, Experimental Methods for the Analysis of Optimization Algorithms.

[28]  Jing-Jing Li,et al.  Multi-swarm particle swarm optimization with multiple learning strategies , 2014, GECCO.

[29]  Hamido Fujita,et al.  Neural-fuzzy with representative sets for prediction of student performance , 2018, Applied Intelligence.

[30]  Mohammad Reza Meybodi,et al.  Multi swarm optimization algorithm with adaptive connectivity degree , 2018, Applied Intelligence.

[31]  Yi Zhou,et al.  A Parallel Genetic Algorithm With Dispersion Correction for HW/SW Partitioning on Multi-Core CPU and Many-Core GPU , 2018, IEEE Access.

[32]  Mehmet Yildirim,et al.  Heuristic Optimization Methods for Generating Test from a Question Bank , 2007, MICAI.

[33]  Bay Vo,et al.  Multi-Swarm Single-Objective Particle Swarm Optimization to Extract Multiple-Choice Tests , 2019, Vietnam. J. Comput. Sci..

[34]  Mohd Zakree Ahmad Nazri,et al.  A multi-swarm particle swarm optimization with local search on multi-robot search system , 2015 .

[35]  Swapnil Prakash Kapse,et al.  An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Adaptive Local Search , 2017, Int. J. Appl. Evol. Comput..

[36]  Graham Kendall,et al.  A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization , 2018, Appl. Soft Comput..

[37]  Jian Xie,et al.  A Multi-Objective Genetic Algorithm Based on Fitting and Interpolation , 2018, IEEE Access.

[38]  Jiahuan Lu,et al.  Online Estimation of State of Power for Lithium-Ion Batteries in Electric Vehicles Using Genetic Algorithm , 2018, IEEE Access.

[39]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[40]  Mohan Krishnamoorthy,et al.  Discrete particle swarm optimization algorithms for two variants of the static data segment location problem , 2018, Applied Intelligence.