Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00053
Xu Zhang, Xin Tian, Bing Yang, Zuyu Zhang, Yan Li
Unlabeled data can be easily collected and help to exploit the correlations among different modalities. Existing works tried to explore label information contained in unlabeled data, however most of them suffer from difficulties in separating samples from different categories and have great interference. This paper proposes a novel method named semi-supervised cross-modal hashing based on label prediction and distance preserving(SS-LPDP). First, we use the deep neural networks to extract the feature of the labeled data among different modalities and get the feature distribution of each category. Second, the similarity of the data among different modalities is maximized based on the extracted feature and the label information. A common objective function is proposed with distance preserving constraint, which can effectively separate data into different categories and reduce interference in retrieval. An optimization algorithm is used to update the network parameters of feature learning in each modality, and the label information of unlabeled data are dynamically updated according to the changes of the feature distribution in each iteration. Experimental evaluation on Wiki, Pascal and NUS-WIDE datasets show that the proposed method outperforms recent methods when we set 25% samples without category labels.
{"title":"Semi-Supervised Cross-Modal Hashing Based on Label Prediction and Distance Preserving","authors":"Xu Zhang, Xin Tian, Bing Yang, Zuyu Zhang, Yan Li","doi":"10.1109/ICTAI.2019.00053","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00053","url":null,"abstract":"Unlabeled data can be easily collected and help to exploit the correlations among different modalities. Existing works tried to explore label information contained in unlabeled data, however most of them suffer from difficulties in separating samples from different categories and have great interference. This paper proposes a novel method named semi-supervised cross-modal hashing based on label prediction and distance preserving(SS-LPDP). First, we use the deep neural networks to extract the feature of the labeled data among different modalities and get the feature distribution of each category. Second, the similarity of the data among different modalities is maximized based on the extracted feature and the label information. A common objective function is proposed with distance preserving constraint, which can effectively separate data into different categories and reduce interference in retrieval. An optimization algorithm is used to update the network parameters of feature learning in each modality, and the label information of unlabeled data are dynamically updated according to the changes of the feature distribution in each iteration. Experimental evaluation on Wiki, Pascal and NUS-WIDE datasets show that the proposed method outperforms recent methods when we set 25% samples without category labels.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114945307","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00191
Nicolas Gutowski, O. Camp, F. Chhel, Tassadit Amghar, Patrick Albers
The pervasive deployment of low cost WiFi access points has accelerated the development of mobile computing to provide ubiquitous computing. Herein, we focus first on the discovery of urban districts, in several french cities, using the connection history of mobile users to a city-wide free public Wi-Fi network. The goal of our approach is to infer relevant spatial context features that can be exploitable by bandit-based recommendation systems and improve their performances. For the unsupervised context reasoning step, we use spectral clustering to deduce areas by grouping Wi-Fi access points according to their users' visitations. We have published an anonymized sample of our dataset and our results on the web. Then, we have integrated the deduced spatial context into a mobile cultural events visualization and recommendation app in order to evaluate the global performance online. Thus, over a year we have observed how such spatial context improves bandit-based recommendations in this app by comparing two use cases of the LinUCB algorithm: the first using the original context without the deduced geo-context, and the second using context enriched by our computed spatial context. Finally, our online evaluation shows that better results are obtained when combining spatial context reasoning with the bandit-based recommendation system, both in terms of accuracy and user participation.
{"title":"Improving Bandit-Based Recommendations with Spatial Context Reasoning: An Online Evaluation","authors":"Nicolas Gutowski, O. Camp, F. Chhel, Tassadit Amghar, Patrick Albers","doi":"10.1109/ICTAI.2019.00191","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00191","url":null,"abstract":"The pervasive deployment of low cost WiFi access points has accelerated the development of mobile computing to provide ubiquitous computing. Herein, we focus first on the discovery of urban districts, in several french cities, using the connection history of mobile users to a city-wide free public Wi-Fi network. The goal of our approach is to infer relevant spatial context features that can be exploitable by bandit-based recommendation systems and improve their performances. For the unsupervised context reasoning step, we use spectral clustering to deduce areas by grouping Wi-Fi access points according to their users' visitations. We have published an anonymized sample of our dataset and our results on the web. Then, we have integrated the deduced spatial context into a mobile cultural events visualization and recommendation app in order to evaluate the global performance online. Thus, over a year we have observed how such spatial context improves bandit-based recommendations in this app by comparing two use cases of the LinUCB algorithm: the first using the original context without the deduced geo-context, and the second using context enriched by our computed spatial context. Finally, our online evaluation shows that better results are obtained when combining spatial context reasoning with the bandit-based recommendation system, both in terms of accuracy and user participation.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114071989","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00213
Sheng Xie, Canlong Zhang, Zhixin Li, Zhiwen Wang
When extracting convolutional features from person images with low resolution, a large amount of available information will be lost due to the pooling, which will lead to the reduction of the accuracy of person classification models. This paper proposes a new classification model, which can effectively to reduce the loss of important information about the convolutional neural works. Firstly, the SE module in the Squeeze-and-Excitation Networks (SENet) is extracted and normalized to generate the Normalized Squeeze-and-Excitation (NSE) module. Then, 4 NSE modules are applied to the convolutional layers of ResNet. Finally, a Sparse Normalized Squeeze-and-Excitation Network (SNSENet) is constructed by adding 4 shortcut connections between the convolutional layers. The experimental results of Market-1501 show that the rank-1 of SNSE-ResNet-50 is 3.7% and 4.2% higher than that of SE-ResNet-50 and ResNet-50 respectively, it has done well in other person re-identification datasets.
{"title":"Sparse High-Level Attention Networks for Person Re-Identification","authors":"Sheng Xie, Canlong Zhang, Zhixin Li, Zhiwen Wang","doi":"10.1109/ICTAI.2019.00213","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00213","url":null,"abstract":"When extracting convolutional features from person images with low resolution, a large amount of available information will be lost due to the pooling, which will lead to the reduction of the accuracy of person classification models. This paper proposes a new classification model, which can effectively to reduce the loss of important information about the convolutional neural works. Firstly, the SE module in the Squeeze-and-Excitation Networks (SENet) is extracted and normalized to generate the Normalized Squeeze-and-Excitation (NSE) module. Then, 4 NSE modules are applied to the convolutional layers of ResNet. Finally, a Sparse Normalized Squeeze-and-Excitation Network (SNSENet) is constructed by adding 4 shortcut connections between the convolutional layers. The experimental results of Market-1501 show that the rank-1 of SNSE-ResNet-50 is 3.7% and 4.2% higher than that of SE-ResNet-50 and ResNet-50 respectively, it has done well in other person re-identification datasets.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122775297","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00244
Tianyang Wang, Jun Huan, Bo Li, Kaoning Hu
Real-world image denoising is a challenging but significant problem in computer vision. Unlike Gaussian denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian denoising approach on real-world denoising problems. In this paper, we propose a simple framework for effective real-world image denoising. Specifically, we investigate the intrinsic properties of the Gaussian denoising prior and demonstrate this prior can aid real-world image denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian denoising prior can be also transferred to real-world image denoising by exploiting appropriate training schemes.
{"title":"Rethink Gaussian Denoising Prior for Real-World Image Denoising","authors":"Tianyang Wang, Jun Huan, Bo Li, Kaoning Hu","doi":"10.1109/ICTAI.2019.00244","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00244","url":null,"abstract":"Real-world image denoising is a challenging but significant problem in computer vision. Unlike Gaussian denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian denoising approach on real-world denoising problems. In this paper, we propose a simple framework for effective real-world image denoising. Specifically, we investigate the intrinsic properties of the Gaussian denoising prior and demonstrate this prior can aid real-world image denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian denoising prior can be also transferred to real-world image denoising by exploiting appropriate training schemes.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176766","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00121
Fangli Ren, Zhengwei Jiang, Jian Liu
Domain generation algorithms (DGA) are employed by malware to generate domain names as a common practice, with which to confirm rendezvous points to their command-and-control (C2) servers. The detection of DGA domain names is one of the important technologies for command and control communication detection. Considering the randomness of the DGA domain names, recent work in DGA detection employed machine learning methods based on features extracting and deep learning architectures to classify domain names. However, these methods perform poorly on wordlistbased DGA families, which generate domain names by randomly concatenating dictionary words. In this paper, we proposed the ATT-CNN-BiLSTM model to detect and classify DGA domain names. Firstly, the Convolutional Neural Network (CNN) and bidirectional Long Short-Term Memory (BiLSTM) neural network layer was used to extract the features of the domain sequences information; secondly, the attention layer was used to allocate the corresponding weight of the extracted domain deep information. Finally, the domain feature messages of different weights were put into the output layer to complete the tasks of detection and classification. The experiment results demonstrate the effectiveness of the proposed model both on regular DGA domain names and wordlist-based ones. To be precise, we got a F1 score of 98.92% for the detection and macro average F1 score of 81% for the classification task of DGA domain names.
{"title":"Integrating an Attention Mechanism and Deep Neural Network for Detection of DGA Domain Names","authors":"Fangli Ren, Zhengwei Jiang, Jian Liu","doi":"10.1109/ICTAI.2019.00121","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00121","url":null,"abstract":"Domain generation algorithms (DGA) are employed by malware to generate domain names as a common practice, with which to confirm rendezvous points to their command-and-control (C2) servers. The detection of DGA domain names is one of the important technologies for command and control communication detection. Considering the randomness of the DGA domain names, recent work in DGA detection employed machine learning methods based on features extracting and deep learning architectures to classify domain names. However, these methods perform poorly on wordlistbased DGA families, which generate domain names by randomly concatenating dictionary words. In this paper, we proposed the ATT-CNN-BiLSTM model to detect and classify DGA domain names. Firstly, the Convolutional Neural Network (CNN) and bidirectional Long Short-Term Memory (BiLSTM) neural network layer was used to extract the features of the domain sequences information; secondly, the attention layer was used to allocate the corresponding weight of the extracted domain deep information. Finally, the domain feature messages of different weights were put into the output layer to complete the tasks of detection and classification. The experiment results demonstrate the effectiveness of the proposed model both on regular DGA domain names and wordlist-based ones. To be precise, we got a F1 score of 98.92% for the detection and macro average F1 score of 81% for the classification task of DGA domain names.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129971873","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00051
Chunfeng Liu, Yan Zhang, Mei Yu, Xuewei Li, Mankun Zhao, Tianyi Xu, Jian Yu, Ruiguo Yu
Knowledge representation learning (KRL), which transforms both the entities and relations into continuous low dimensional continuous vector space, has attracted considerable research. Most of existing knowledge graph (KG) completion models only considers the structural representation of triples, but do not consider the important text information about entity descriptions in the knowledge base. We propose a text-enhanced KG model based on gated convolution network (GConvTE), which can learn entity descriptions and symbol triples jointly by feature fusion. Specifically, each triple (head entity, relation, tail entity) is represented as a 3-column structural embedding matrix, a 3-column textual embedding matrix and a 3-column joint embedding matrix where each column vector represents a triple element. Textual embeddings are obtained by bidirectional gated recurrent unit with attention (A-BGRU) encoding entity descriptions and joint embeddings are obtained by the combination of textual embeddings and structural embeddings. Extending feature dimension in embedding layer, these three matrixs are concatenated into 3-channel feature block to be fed into convolution layer, where the gated unit is added to selectively output the joint features maps. These feature maps are concatenated and then multiplied with a weight vector via a dot product to return a score. The experimental results show that our model GConvTE achieves better link performance than previous state-of-art embedding models on two benchmark datasets.
{"title":"Text-Enhanced Knowledge Representation Learning Based on Gated Convolutional Networks","authors":"Chunfeng Liu, Yan Zhang, Mei Yu, Xuewei Li, Mankun Zhao, Tianyi Xu, Jian Yu, Ruiguo Yu","doi":"10.1109/ICTAI.2019.00051","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00051","url":null,"abstract":"Knowledge representation learning (KRL), which transforms both the entities and relations into continuous low dimensional continuous vector space, has attracted considerable research. Most of existing knowledge graph (KG) completion models only considers the structural representation of triples, but do not consider the important text information about entity descriptions in the knowledge base. We propose a text-enhanced KG model based on gated convolution network (GConvTE), which can learn entity descriptions and symbol triples jointly by feature fusion. Specifically, each triple (head entity, relation, tail entity) is represented as a 3-column structural embedding matrix, a 3-column textual embedding matrix and a 3-column joint embedding matrix where each column vector represents a triple element. Textual embeddings are obtained by bidirectional gated recurrent unit with attention (A-BGRU) encoding entity descriptions and joint embeddings are obtained by the combination of textual embeddings and structural embeddings. Extending feature dimension in embedding layer, these three matrixs are concatenated into 3-channel feature block to be fed into convolution layer, where the gated unit is added to selectively output the joint features maps. These feature maps are concatenated and then multiplied with a weight vector via a dot product to return a score. The experimental results show that our model GConvTE achieves better link performance than previous state-of-art embedding models on two benchmark datasets.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620379","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00185
Pavel Rytír, L. Chrpa, B. Bosanský
Many problems from classical planning are applied in the environment with other, possibly adversarial agents. However, plans found by classical planning algorithms lack the robustness against the actions of other agents - the quality of computed plans can be significantly worse compared to the model. To explicitly reason about other (adversarial) agents, the game-theoretic framework can be used. The scalability of game-theoretic algorithms, however, is limited and often insufficient for real-world problems. In this paper, we combine classical domain-independent planning algorithms and game-theoretic strategy-generation algorithm where plans form strategies in the game. Our contribution is threefold. First, we provide the methodology for using classical planning in this game-theoretic framework. Second, we analyze the trade-off between the quality of the planning algorithm and the robustness of final randomized plans and the computation time. Finally, we analyze different variants of integration of classical planning algorithms into the game-theoretic framework and show that at the cost a minor loss in the robustness of final plans, we can significantly reduce the computation time.
{"title":"Using Classical Planning in Adversarial Problems","authors":"Pavel Rytír, L. Chrpa, B. Bosanský","doi":"10.1109/ICTAI.2019.00185","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00185","url":null,"abstract":"Many problems from classical planning are applied in the environment with other, possibly adversarial agents. However, plans found by classical planning algorithms lack the robustness against the actions of other agents - the quality of computed plans can be significantly worse compared to the model. To explicitly reason about other (adversarial) agents, the game-theoretic framework can be used. The scalability of game-theoretic algorithms, however, is limited and often insufficient for real-world problems. In this paper, we combine classical domain-independent planning algorithms and game-theoretic strategy-generation algorithm where plans form strategies in the game. Our contribution is threefold. First, we provide the methodology for using classical planning in this game-theoretic framework. Second, we analyze the trade-off between the quality of the planning algorithm and the robustness of final randomized plans and the computation time. Finally, we analyze different variants of integration of classical planning algorithms into the game-theoretic framework and show that at the cost a minor loss in the robustness of final plans, we can significantly reduce the computation time.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130880561","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00166
Hongli Wang, Jiangtao Ren
How to generate relevant and informative response is one of the core topics in response generation area. Following the task formulation of neural machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. However, the dialogue model tends to generate safe, commonplace responses (e.g., I don't know) regardless of the input, when learning to maximize the likelihood of response for the given message in an almost loss-less manner just like MT. Different from existing works, we propose a Global-Local Selective Encoding model (GLSE) to extend the seq2seq framework to generate more relevant and informative responses. Specifically, two types of selective gate network are introduced in this work: (i) A local selective word-sentence gate is added after encoding phase of Seq2Seq learning framework, which learns to tailor the original message information and generates a selected input representation. (ii) A global selective bidirectional-context gate is set to control the bidirectional information flow from a BiGRU based encoder to decoder. Empirical studies indicate the advantage of our model over several classical and strong baselines.
{"title":"GLSE: Global-Local Selective Encoding for Response Generation in Neural Conversation Model","authors":"Hongli Wang, Jiangtao Ren","doi":"10.1109/ICTAI.2019.00166","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00166","url":null,"abstract":"How to generate relevant and informative response is one of the core topics in response generation area. Following the task formulation of neural machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. However, the dialogue model tends to generate safe, commonplace responses (e.g., I don't know) regardless of the input, when learning to maximize the likelihood of response for the given message in an almost loss-less manner just like MT. Different from existing works, we propose a Global-Local Selective Encoding model (GLSE) to extend the seq2seq framework to generate more relevant and informative responses. Specifically, two types of selective gate network are introduced in this work: (i) A local selective word-sentence gate is added after encoding phase of Seq2Seq learning framework, which learns to tailor the original message information and generates a selected input representation. (ii) A global selective bidirectional-context gate is set to control the bidirectional information flow from a BiGRU based encoder to decoder. Empirical studies indicate the advantage of our model over several classical and strong baselines.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130164664","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00113
Xinyu Zhou, Yiwen Ling, M. Zhong, Mingwen Wang
As a relatively new paradigm of bio-inspired optimization techniques, artificial bee colony (ABC) algorithm has attracted much attention for its simplicity and effectiveness. However, the performance of ABC is not satisfactory when solving some complex optimization problems. To improve its performance, we propose a novel ABC variant by designing a dynamic multi-population scheme (DMPS). In DMPS, the population is divided into several subpopulations, and the size of subpopulation is adjusted dynamically by checking the quality of the global best solution. Moreover, we design two novel solution search equations to maximize the effectiveness of DMPS, in which the local best solution of each subpopulation and the global best solution of the whole population are utilized simultaneously. In the experiments, 32 widely used benchmark functions are used, and four well-established ABC variants are involved in the comparison. The comparative results show that our approach performs better on the majority of benchmark functions.
{"title":"Dynamic Multi-population Artificial Bee Colony Algorithm","authors":"Xinyu Zhou, Yiwen Ling, M. Zhong, Mingwen Wang","doi":"10.1109/ICTAI.2019.00113","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00113","url":null,"abstract":"As a relatively new paradigm of bio-inspired optimization techniques, artificial bee colony (ABC) algorithm has attracted much attention for its simplicity and effectiveness. However, the performance of ABC is not satisfactory when solving some complex optimization problems. To improve its performance, we propose a novel ABC variant by designing a dynamic multi-population scheme (DMPS). In DMPS, the population is divided into several subpopulations, and the size of subpopulation is adjusted dynamically by checking the quality of the global best solution. Moreover, we design two novel solution search equations to maximize the effectiveness of DMPS, in which the local best solution of each subpopulation and the global best solution of the whole population are utilized simultaneously. In the experiments, 32 widely used benchmark functions are used, and four well-established ABC variants are involved in the comparison. The comparative results show that our approach performs better on the majority of benchmark functions.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116179845","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}
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