Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine algorithms

Lenin, K.

doi:10.1007/s13198-021-01399-y

Cited by 4 publications

(1 citation statement)

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“…The first one is serial mixing, where a group of recommended algorithms are connected in series. The former recommendation result is the input of the latter recommendation algorithm, and the latter recommendation algorithm optimizes the former recommendation result; The second is graded mixing [11][12]. The implementation steps of pipelining design are shown in Figure 2.…”

Section: Flow Typementioning

confidence: 99%

Collaborative Filtering Algorithm based on Hybrid Machine Learning Optimization

2020

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Under the influence of the mobile Internet era, users' patience is increasingly limited. Future recommendation algorithms should quickly respond to users' urgent needs to save users' time. Under the background of big data, how to ensure the relatively low complexity and high accuracy of information push and display is a very valuable topic. Therefore, this paper discusses and analyzes the collaborative filtering algorithm (CFA) based on hybrid machine learning (ML) optimization. In this paper, the research background and significance of CFA are first described, including the development status of collaborative filtering recommendation algorithm, the research status of association rule recommendation algorithm and particle swarm optimization algorithm, and the problems in collaborative filtering recommendation algorithm. A new hybrid CFA model is proposed based on the hybrid ML optimization. This paper designs a simulation table of three recommendation algorithms to verify the proposed CFA. The experiment shows that the recall rate of the CFA proposed in this paper based on the hybrid ML optimization is not lower than other recommendation algorithms, which verifies the effectiveness of this algorithm.

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Section: Flow Typementioning

confidence: 99%