Particle swarm optimization with query-based learning for multi-objective power contract problem

Chang, Ray-I; Lin, Shu‐Yu; Hung, Yao-Min

doi:10.1016/j.eswa.2011.08.175

Cited by 8 publications

(8 citation statements)

References 25 publications

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“…Query‐based learning actively and repeatedly trains students as well as provides them with learning suggestions. This concept has been applied in several models (Chang, Lin & Hung, ). Collaborative learning is when students learn together to explore questions or create meaningful projects.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Survey of learning experiences and influence of learning style preferences on user intentions regarding MOOCs

Chang¹,

Hung²,

Lin³

2015

Brit J Educational Tech

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With the rapid development of web techniques, information and communication technology is being increasingly used in curricula, and learning portfolios can be automatically retrieved and maintained as learners interact through e-learning platforms.Further, massive open online courses (MOOCs), which apply such technology to provide open access to hundreds and thousands of learners, have recently gained popularity in higher education. Learning styles refer to an individual's approach to learning based on their preferences, strengths and weaknesses. It was speculated that different learning styles could affect a learner's preference for MOOCs. Therefore, in this study, we investigated the influence of learning styles on the learners' intentions to use MOOCs. The participants comprised 184 undergraduate students, who were surveyed regarding their learning experiences, motivation and intentions to use MOOCs. The results showed that learners with a high-reflective learning style tended to have less experience in using MOOCs. Using the K-means algorithm, we also clustered the multiple learning styles and found a 90% probability that the type of learning styles does influence learner intentions. Finally, we explored the reasons why learners used MOOCs, analyzed the learning approaches that could assist learning in MOOCs and examined the correlation between learning styles and user intention.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Survey of learning experiences and influence of learning style preferences on user intentions regarding MOOCs

Chang¹,

Hung²,

Lin³

2015

Brit J Educational Tech

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“…Linear regression showed a better prediction performance than the other algorithms for product quality prediction. According to previous studies, query-based learning exhibits an excellent computing performance because of its advantages in generating new learning samples and training the model with different parameters [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. This study obtained similar results.…”

Section: Resultsmentioning

confidence: 99%

“…This study proposes query-based learning, which takes advantage of the generative and interactive reinforcement methods by creating new learning instances and bi-direction interactions in the solution space. Query-based learning [ 45 , 46 , 47 , 48 , 49 ] is an active learning technique that effectively enhances the model performance by incorporating key data points. Figure 4 illustrates the concept of query-based learning using a simple binary classification example.…”

Section: Methodsmentioning

confidence: 99%

“… Step 5. According to the two learning strategies, by adding samples to the DD, the training sample set consists of either (a) samples of the most correct prediction, which is learning from the strong learners [ 47 , 49 ], or (b) samples of the least correct prediction, for which the weak learners are used to explore the fuzzy or unexplored solution spaces [ 48 ]. …”

Section: Methodsmentioning

confidence: 99%

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Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing

Chang,

Lin,

Hung

2024

Sensors

Self Cite

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In traditional textile manufacturing, downstream manufacturers use raw materials, such as Nylon and cotton yarns, to produce textile products. The manufacturing process involves warping, sizing, beaming, weaving, and inspection. Staff members typically use a trial-and-error approach to adjust the appropriate production parameters in the manufacturing process, which can be time consuming and a waste of resources. To enhance the efficiency and effectiveness of textile manufacturing economically, this study proposes a query-based learning method in regression analytics using existing manufacturing data. Query-based learning allows the model training to evolve its decision-making process through dynamic interactions with its solution space. In this study, predefined target parameters of quality factors were first used to validate the training results and create new training patterns. These new patterns were then imported into the solution space of the training model. In predicting product quality, the results show that the proposed query-based regression algorithm has a mean squared error of 0.0153, which is better than those of the original regression-related methods (Avg. mean squared error = 0.020). The trained model was deployed as an application programing interface (API) for cloud-based analytics and an extensive auto-notification service.

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“…In this paper, a query-based learning (QBL) approach [8,9] is adopted to prevent PSO from falling into a local optimum. QBL follows a very basic rule: ''to teach students in accordance with their aptitude.''…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Combined with Query-Based Learning Using MapReduce

Lin

Chi

Chang

2014

Lecture Notes in Electrical Engineering

Self Cite

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Abstract. Particle swarm optimization (PSO) has shown its effectiveness to solve many complex optimization problems. However, PSO sometimes may fall into a local optimal solution rather than the global optimal solution of a given problem. For large-scale optimization problems, several parallelized PSOs have been proposed in the literature. In this paper, a query-based learning (QBL) approach is adopted to help PSO jump out of a local optimum. An Oracle is introduced that answers PSO whether there are too many particles in a flat region of the solution space. If yes, the algorithm will redistribute some of the particles in the flat region to somewhere else. A parallelized implementation, referred to as MRPSO-QBL, is developed in the Apache Hadoop MapReduce framework, as the framework provides a simpler and better parallel programming paradigm. The experiment results on several benchmark functions have demonstrated that MRPSO-QBL can find better solutions and converge faster.

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Particle swarm optimization with query-based learning for multi-objective power contract problem

Cited by 8 publications

References 25 publications

Survey of learning experiences and influence of learning style preferences on user intentions regarding MOOCs

Survey of learning experiences and influence of learning style preferences on user intentions regarding MOOCs

Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing

Particle Swarm Optimization Combined with Query-Based Learning Using MapReduce

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