2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)最新文献_第2页

Evolutionary multi-objective algorithms for feed resource allocation in dairy systems 乳品系统中饲料资源分配的进化多目标算法

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769787

G. Notte, P. Chilibroste, M. Pedemonte, Héctor Cancela

{"title":"Evolutionary multi-objective algorithms for feed resource allocation in dairy systems","authors":"G. Notte, P. Chilibroste, M. Pedemonte, Héctor Cancela","doi":"10.1109/LA-CCI48322.2021.9769787","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769787","url":null,"abstract":"In grassland-based dairy systems, determining how to rotate the cows among fields for grazing, how much concentrate to supply and the correct stocking rate to be used are important decisions that impact on the efficiency of the system. Considering the presence of conflictive objectives, a multi-objective approach is therefore the natural way of facing the problem. Due to the computational difficulty of finding the full solution set (the Pareto front) of multi-objective models, it is usually necessary to employ algorithms giving a good approximation of this set. In particular, a number of multi-objective evolutionary algorithms with different characteristics have been proposed in the general optimization literature; but there is no current study of which is the most appropriate one for feed resource allocation in dairy systems. In this work, we present the performance evaluation of four multi-objective evolutionary algorithms to generate an approximation of the Pareto front of the feed resource allocation problem in dairy systems. Two classical genetic algorithms (NSGA-II and SPEA-2) and two differential evolution (DE) algorithms (GDE-3 and a Pareto-based DE) were used. To evaluate the algorithms, two experiments based on scenarios constructed from real data were performed. The comparison took into account running times, objective function values attained, Pareto front comparisons, and approximation quality measures based on four different metrics. From the results we conclude that the SPEA-2 is the algorithm that obtains the best quality performance for the problem under study, but also the slowest one, opening a future work opportunity of improving its computational performance.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115499703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards best default configuration settings for NMPSO in multi-objective optimization 面向NMPSO在多目标优化中的最佳默认配置设置

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769844

Rodrigo Marinao-Rivas, M. Zambrano-Bigiarini

{"title":"Towards best default configuration settings for NMPSO in multi-objective optimization","authors":"Rodrigo Marinao-Rivas, M. Zambrano-Bigiarini","doi":"10.1109/LA-CCI48322.2021.9769844","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769844","url":null,"abstract":"In this work we tested different configuration settings for the NMPSO algorithm, aiming at solving multi-objective optimization problems with a small number of function evaluations, which is an important aspect that must be addressed in real-world optimization problems. Sixteen different configurations were tested for NMPSO, with different combinations of: i) the swarm size, ii) the maximum number of particles in the external archive, and iii) the maximum amount of genetic operations in the external archive. Three DTLZ problems were used to select the best configuration, which was then evaluated against other state-of-the-art multi-objective optimization algorithms (MMOPSO, NSGA-II, NSGA-III). Our results showed that the fastest convergence towards the true Pareto-optimal front is provided by the configuration with a swarm size of 10, a maximum number of particles allowed in the external archive of 100, and a limit of genetic operations per iteration given by 50% of the maximum number of particles allowed in the external archive. The selected configuration was also very competitive or even superior against NSGA-II and NSGA-III, in terms of the number of function evaluations required to start having an HV larger than zero, but also in the HV values achieved after stabilization of the Pareto-optimal front.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117077324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling the Social Interactions in Grey Wolf Optimizer 灰狼优化器中的社会互动建模

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769781

R. Lira, M. Macedo, H. Siqueira, R. Menezes, C. J. A. B. Filho

{"title":"Modelling the Social Interactions in Grey Wolf Optimizer","authors":"R. Lira, M. Macedo, H. Siqueira, R. Menezes, C. J. A. B. Filho","doi":"10.1109/LA-CCI48322.2021.9769781","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769781","url":null,"abstract":"Swarm Intelligence has been successfully used for solving high-dimensional and multimodal optimization problems. However, the wide range of swarm-based techniques, operators, and parameters requires prior knowledge before applying them to real-world problems. Because of this, we have been studying the meso-level characteristics that emerge from the social interactions within the swarm to understand each swarm-based technique’s unique characteristics. In this paper, we model and study the interaction network of the Grey Wolf Optimizer (GWO) to capture its social behaviour. We used Portrait divergence to compare the similarity between network structures over experiments, simulations and iterations of the GWO. We also used Kullback divergence to compare the probability distributions of the network flows varying over experiments, simulations and iterations of the GWO. Furthermore, we discovered we could identify the GWO convergence using the interaction network approach. Comparing different simulations, we found that the wolves communicate using a stable network structure but not necessarily a stable network flow indicating variance in the number of highly influential wolves. We also point out patterns found in GWO that appears to be similar to other swarm-based algorithms (GPSO and FSS).","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130958864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sentiment Analysis on Brazilian Portuguese User Reviews 巴西葡萄牙语用户评论的情感分析

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769838

F. Souza, Joao Filho

{"title":"Sentiment Analysis on Brazilian Portuguese User Reviews","authors":"F. Souza, Joao Filho","doi":"10.1109/LA-CCI48322.2021.9769838","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769838","url":null,"abstract":"Sentiment Analysis is one of the most classical and primarily studied natural language processing tasks. This problem had a notable advance with the proposition of more complex and scalable machine learning models. Despite this progress, the Brazilian Portuguese language still disposes only of limited linguistic resources, such as datasets dedicated to sentiment classification, especially when considering the existence of predefined partitions in training, testing, and validation sets that would allow a more fair comparison of different algorithm alternatives. Motivated by these issues, this work analyzes the predictive performance of a range of document embedding strategies, assuming the polarity as the system outcome. This analysis includes five sentiment analysis datasets in Brazilian Portuguese, unified in a single dataset, and a reference partitioning in training, testing, and validation sets, both made publicly available through a digital repository. A cross-evaluation of dataset-specific models over different contexts is conducted to evaluate their generalization capabilities and the feasibility of adopting a unique model for addressing all scenarios.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124140461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Hierarchical Ensemble of LSTM-based Autoencoders for Novelty Detection in Passive Sonar Systems 被动声呐系统中基于lstm的自编码器分层集成新颖性检测

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769821

Eduardo Sperle Honorato, J. B. O. S. Filho, Victor Hugo da Silva Muniz

引用次数: 1

A novel Encoder-Decoder structure for Time Series analysis based on Bayesian Uncertainty reduction 一种新的基于贝叶斯不确定性约简的时间序列编码器-解码器结构

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769850

Ricardo Xavier Llugsi Cañar, S. Yacoubi, Allyx Fontaine, P. Lupera

引用次数: 2

Neural Sliding Mode Block Control of Single-Phase Induction Motors 单相感应电动机的神经滑模块控制

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769817

J. P. Vega, E. Sánchez, A. Loukianov, Larbi Djilali

引用次数: 0

A Hybrid Model With Error Correction for Wind Speed Forecasting 一种带误差修正的风速预报混合模型

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769818

Tathiana M. Barchi, L. F. P. Costa, E. Puchta, M. Martins, M. L. Andrade, P. S. D. M. Neto, H. Siqueira

引用次数: 0

Classification of Respiration Sounds Using Deep Pre-trained Audio Embeddings 使用深度预训练音频嵌入的呼吸声音分类

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769831

Carlos A. Galindo Meza, Juan A. del Hoyo Ontiveros, P. López-Meyer

引用次数: 0

Using Deep Learning Predictors and Latent Dirichlet Allocation to Identify Key Issues Affecting Clients in Chilean Restaurants 使用深度学习预测器和潜在狄利克雷分配来确定影响智利餐馆客户的关键问题

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769840

A. Ferreira, Walter Gómez, Ronald Kliebs

引用次数: 0