Application of Improved Ant Colony Algorithm in Optimal Planning of Dynamic Distribution Network

Liu, JinXiong; Huang, Tiezheng; Duan, Xi

doi:10.1088/1742-6596/1533/3/032068

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…(1) Classify the text data and name entity recognition (2) Filter stop words and delete useless interjections, modal particle, personal pronouns, and so on [21] (3) Extract keywords and use TF-IDF algorithm to select key information (4) For text representation, use the word embedding tool to complete the word embedding of keywords and obtain the vectorization expression of keywords (5) For text clustering, adopt cosine similarity calculation method to cluster document vector (6) Analyze clustering results…”

Section: Model Constructionmentioning

confidence: 99%

Construction of Alumni Information Analysis Model Based on Big Data

Wang

2022

Mathematical Problems in Engineering

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In order to integrate and utilize alumni resources in a better way, big data is utilized to construct alumni information analysis model based on improved hierarchical clustering algorithm, so as to realize mining and retrieval of alumni information. First,the basic principle of hierarchical clustering algorithm is analyzed concretely. Moreover, the improvement is performed on this basis, and a method of calculating the distance between class clusters based on the ant colony optimization is proposed, which uses the shortest distance of the ant colony algorithm to optimally solve the distance between hierarchical class clusters, so as to improve the clustering accuracy. Then, the alumni information analysis model based on improved hierarchical clustering algorithm is constructed, and the model is divided into text preprocessing, keyword extraction, text feature vector generation, name disambiguation, and alumni recognition modules. Finally, the improved hierarchical clustering algorithm and construction of model are verified by experiments. The results show that the accuracy of the improved agglomerative hierarchical clustering algorithm is as high as 86.4% on average and 3.8% and 4.8% more than the two traditional algorithms. Thus, the clustering effect of the algorithm is better, and the proposed alumni analysis model can effectively process text disambiguation of web pages and identification of alumni information, which has certain effectiveness.

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Section: Model Constructionmentioning

confidence: 99%

Construction of Alumni Information Analysis Model Based on Big Data

Wang

2022

Mathematical Problems in Engineering

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An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

Rezvanian¹,

Vahidipour²,

Sadollah³

2024

Optimization Algorithms - Classics and Recent Advances

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Swarm intelligence is a relatively recent approach for solving optimization problems that usually adopts the social behavior of birds and animals. The most popular class of swarm intelligence is ant colony optimization (ACO), which simulates the behavior of ants in seeking and moving food. This chapter aim to briefly overview the important role of ant colony optimization methods in solving optimization problems in time-varying and dynamic environments. To this end, we describe concisely the dynamic optimization problems, challenges, methods, benchmarks, measures, and a brief review of methodologies designed using the ACO and its variants. Finally, a short bibliometric analysis is given for the ACO and its variants for solving dynamic optimization problems.

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Application of Improved Ant Colony Algorithm in Optimal Planning of Dynamic Distribution Network

Cited by 2 publications

References 2 publications

Construction of Alumni Information Analysis Model Based on Big Data

Construction of Alumni Information Analysis Model Based on Big Data

An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

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