Particle swarm optimization in multi-agent system for the intelligent generation of test papers

Chen, Peng; Meng, Anbo; Zhao, Chunming

doi:10.1109/cec.2008.4631085

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…The authors also implemented a parallel variant of the proposed PSO algorithm which is more efficient but more complicated. Another study that combines the PSO with MAS and GAs was proposed in [17]. The authors suggest the integration of the mentioned approaches to solve the problem of automatic generation of test paper in a parallel computing environment.…”

Section: Related Work On the Hybridization Of Pso And Masmentioning

confidence: 99%

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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h i g h l i g h t s• Proving the efficiency of optimization algorithms in solving real-world problems.• A new concept of accent birds introduced to the particle swarm optimization. • A new hybrid scheme that integrates PSO and MAS.• The hybridization improves the performance of the original tested algorithms. • A comparison between simulation and experimental validation is given. a r t i c l e i n f o Keywords: Accent based PSO Multi-agent Many-objective optimization Experimental validation 3D indoor deployment DL-IoT collection networks a b s t r a c tThe 3D indoor deployment of sensor nodes is a complex real world problem, proven to be NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid approach relying on a novel bird's accent-based many objective particle swarm optimization algorithm (named acMaPSO) to resolve the problem of 3D indoor deployment on the Internet of Things collection networks. The new concept of bird's accent is presented to assess the search ability of particles in their local areas. To conserve the diversity of the population during searching, particles are separated into different accent groups by their regional habitation and are classified into different categories of birds/particles in each cluster according to their common manner of singing. A particle in an accent-group can select other particles as its neighbors from its group or from other groups (which sing differently) if the selected particles have the same expertise in singing or are less experienced compared to this particle. To allow the search escaping from local optima, the most expert particles (parents) ''die'' and are regularly replaced by a novice (newborn) randomly generated ones. Moreover, the hybridization of the proposed acMaPSO algorithm with multi-agent systems is suggested. The new variant (named acMaMaPSO) takes advantage of the distribution and interactivity of particle agents. Experimental, numerical and statistical found results show the effectiveness of the two proposed variants compared to different other recent state-of-the-art of many-objective evolutionary algorithms.✩ This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. * Correspondence to: bureau C107, Batiment C, 1 Place Georges positions and the topology of the network. In this study, we are interested in the deployment of nodes in a three-dimensional space that reflects the real topology of the RoI (region of interest) better than the two-dimensional case. We are particularly interested in improving the initial 3D indoor deployment by adding new nodes while optimizing a set of objectives such as coverage, connectivity, localization, quality of links and network utilization. These objectives will be detailed in the modeling section. To resolve the deployment problem in WSN, the topology of the network can be modeled as an identification problem in a graph as addressed in [1] and [2]. In this static case, the deployment algorithm is run off-...

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Section: Related Work On the Hybridization Of Pso And Masmentioning

confidence: 99%

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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A Novel Online Test-Sheet Composition Approach Using Genetic Algorithm

Wang

Pan

et al. 2009

Advances in Computation and Intelligence

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Abstract. In e-learning environment, online testing system can help to evaluate students' learning status precisely. To meet the users' multiple assessment requirements, a new test-sheet composition model was put forward. Based on the proposed model, a genetic algorithm with effective coding strategy and problem characteristic mutation operation were designed to generate high quality testsheet in online testing systems. The proposed algorithm was tested using a series of item banks with different scales. Superiority of the proposed algorithm is demonstrated by comparing it with the genetic algorithm with binary coding strategy.

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A Personalized Genetic Algorithm Approach for Test Sheet Assembling

Niu

Chen

et al. 2011

Advances in Web-Based Learning - ICWL 2011

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Particle swarm optimization in multi-agent system for the intelligent generation of test papers

Cited by 6 publications

References 10 publications

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

A Novel Online Test-Sheet Composition Approach Using Genetic Algorithm

A Personalized Genetic Algorithm Approach for Test Sheet Assembling

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