首页 > 最新文献

2019 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献

英文 中文
Domination-Based Ordinal Regression for Expensive Multi-Objective Optimization 基于支配的有序回归的昂贵多目标优化
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002828
Xunzhao Yu, X. Yao, Yan Wang, Ling Zhu, Dimitar Filev
Most surrogate-assisted evolutionary algorithms save expensive evaluations by approximating fitness functions. However, many real-world applications are high-dimensional multi-objective expensive optimization problems, and it is difficult to approximate their fitness functions accurately using a very limited number of fitness evaluations. This paper proposes a domination-based ordinal regression surrogate, in which a Kriging model is employed to learn the domination relationship values and to approximate the ordinal landscape of fitness functions. Coupling with a hybrid surrogate management strategy, the solutions with higher probabilities to dominate others are selected and evaluated in fitness functions. Our empirical studies on the DTLZ testing functions demonstrate that the proposed algorithm is more efficient when compared with other state-of-the-art expensive multi-objective optimization methods.
大多数代理辅助进化算法通过逼近适应度函数来节省昂贵的评估。然而,现实世界中的许多应用都是高维多目标昂贵的优化问题,使用非常有限的适应度评估很难准确地近似其适应度函数。本文提出了一种基于支配的有序回归代理,利用Kriging模型学习支配关系值,逼近适应度函数的有序格局。结合混合代理管理策略,选择具有较高支配概率的解决方案,并在适应度函数中进行评估。我们对DTLZ测试函数的实证研究表明,与其他最先进的昂贵的多目标优化方法相比,本文提出的算法效率更高。
{"title":"Domination-Based Ordinal Regression for Expensive Multi-Objective Optimization","authors":"Xunzhao Yu, X. Yao, Yan Wang, Ling Zhu, Dimitar Filev","doi":"10.1109/SSCI44817.2019.9002828","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002828","url":null,"abstract":"Most surrogate-assisted evolutionary algorithms save expensive evaluations by approximating fitness functions. However, many real-world applications are high-dimensional multi-objective expensive optimization problems, and it is difficult to approximate their fitness functions accurately using a very limited number of fitness evaluations. This paper proposes a domination-based ordinal regression surrogate, in which a Kriging model is employed to learn the domination relationship values and to approximate the ordinal landscape of fitness functions. Coupling with a hybrid surrogate management strategy, the solutions with higher probabilities to dominate others are selected and evaluated in fitness functions. Our empirical studies on the DTLZ testing functions demonstrate that the proposed algorithm is more efficient when compared with other state-of-the-art expensive multi-objective optimization methods.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"26-27 1-3 1","pages":"2058-2065"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78306713","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: 6
Evolving Ensembles of Routing Policies using Genetic Programming for Uncertain Capacitated Arc Routing Problem 基于遗传规划的不确定电容弧线路由策略演化集成
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002749
Shaolin Wang, Yi Mei, John Park, Mengjie Zhang
The Uncertain Capacitated Arc Routing Problem (UCARP) has a wide range of real-world applications. Genetic Programming Hyper-heuristic (GPHH) approaches have shown success in solving UCARP to evolve routing policies that generate routes in real time. However, existing GPHH approaches still have a drawback. Despite the effectiveness in many benchmarks, the single routing policy evolved by GPHH is too complex to interpret. On the other hand, the users need to be able to understand the evolved routing policies to feel confident to use them. In this paper, we aim to employ three ensemble methods, BaggingGP, BoostingGP and Cooperative Co-evolution GP (CCGP) to evolve a group of interpretable routing policies. The ensemble can be used to compare with single complex routing policy from GPHH. Experiment studies show that CCGP significantly outperformed BaggingGP and BoostingGP, and can generate much smaller and simpler routing policies to form ensembles with comparable test performance as the routing policy evolved by SimpleGP. This demonstrates the potential of improving the interpretability issue of GPHH using ensemble methods.
不确定电容电弧路由问题(UCARP)具有广泛的实际应用。遗传规划超启发式(GPHH)方法已经成功地解决了实时生成路由的路由策略。然而,现有的GPHH方法仍然有一个缺点。尽管GPHH在许多基准测试中是有效的,但是由GPHH演变的单一路由策略太复杂而无法解释。另一方面,用户需要能够理解进化的路由策略,以便有信心使用它们。本文采用baginggp、BoostingGP和Cooperative Co-evolution GP (CCGP)三种集成方法来演化一组可解释的路由策略。该集合可用于与GPHH的单个复杂路由策略进行比较。实验研究表明,CCGP明显优于baginggp和BoostingGP,并且可以生成更小、更简单的路由策略,形成与SimpleGP进化的路由策略具有相当测试性能的集成。这证明了使用集成方法改善GPHH可解释性问题的潜力。
{"title":"Evolving Ensembles of Routing Policies using Genetic Programming for Uncertain Capacitated Arc Routing Problem","authors":"Shaolin Wang, Yi Mei, John Park, Mengjie Zhang","doi":"10.1109/SSCI44817.2019.9002749","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002749","url":null,"abstract":"The Uncertain Capacitated Arc Routing Problem (UCARP) has a wide range of real-world applications. Genetic Programming Hyper-heuristic (GPHH) approaches have shown success in solving UCARP to evolve routing policies that generate routes in real time. However, existing GPHH approaches still have a drawback. Despite the effectiveness in many benchmarks, the single routing policy evolved by GPHH is too complex to interpret. On the other hand, the users need to be able to understand the evolved routing policies to feel confident to use them. In this paper, we aim to employ three ensemble methods, BaggingGP, BoostingGP and Cooperative Co-evolution GP (CCGP) to evolve a group of interpretable routing policies. The ensemble can be used to compare with single complex routing policy from GPHH. Experiment studies show that CCGP significantly outperformed BaggingGP and BoostingGP, and can generate much smaller and simpler routing policies to form ensembles with comparable test performance as the routing policy evolved by SimpleGP. This demonstrates the potential of improving the interpretability issue of GPHH using ensemble methods.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"20 1","pages":"1628-1635"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72906817","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: 12
Toward Human-like Billiard AI Bot Based on Backward Induction and Machine Learning 基于逆向归纳和机器学习的仿人台球人工智能机器人研究
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003085
Kuei Gu Tung, Sheng Wen Wang, Wen-Kai Tai, Der-Lor Way, Chinchen Chang
A human-like billiard AI bot approach is proposed in this paper. We analyzed actual game records of human players to obtain feature vectors. The Backward Induction algorithm and machine learning are then proposed to imitate decisions by human players. A run-out sequence is searched backwardly with the assists from heuristics and predictions of neural network models. Through the planning process, a strike unit is found to help guide the physics simulator. With our AI suggestion of strategies, it avoids being over-dependent on the robust and precise physics simulation. Also, we defined an appropriate approach to gauge the human likeness of AI and evaluate our proposed methods. The experimental results show that our method overall is more similar to the way how human players play than that of original AI.
本文提出了一种类人台球人工智能机器人方法。我们通过分析人类玩家的实际游戏记录来获得特征向量。然后提出了逆向归纳算法和机器学习来模仿人类玩家的决策。在神经网络模型的启发式和预测的帮助下,对运行序列进行反向搜索。通过规划过程,找到一个打击单元来帮助指导物理模拟器。通过我们的AI建议策略,它可以避免过度依赖于稳健和精确的物理模拟。此外,我们定义了一种适当的方法来衡量人工智能的人类相似性并评估我们提出的方法。实验结果表明,我们的方法总体上更接近于人类玩家的游戏方式,而不是原始AI。
{"title":"Toward Human-like Billiard AI Bot Based on Backward Induction and Machine Learning","authors":"Kuei Gu Tung, Sheng Wen Wang, Wen-Kai Tai, Der-Lor Way, Chinchen Chang","doi":"10.1109/SSCI44817.2019.9003085","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003085","url":null,"abstract":"A human-like billiard AI bot approach is proposed in this paper. We analyzed actual game records of human players to obtain feature vectors. The Backward Induction algorithm and machine learning are then proposed to imitate decisions by human players. A run-out sequence is searched backwardly with the assists from heuristics and predictions of neural network models. Through the planning process, a strike unit is found to help guide the physics simulator. With our AI suggestion of strategies, it avoids being over-dependent on the robust and precise physics simulation. Also, we defined an appropriate approach to gauge the human likeness of AI and evaluate our proposed methods. The experimental results show that our method overall is more similar to the way how human players play than that of original AI.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"86 1","pages":"924-932"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73152247","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: 2
Computer Vision for Detecting and Measuring Multicellular Tumor Shperoids of Prostate Cancer 前列腺癌多细胞类肿瘤的计算机视觉检测与测量
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002908
Alex Wojaczek, Regina-Veronicka Kalaydina, Mohammed Gasmallah, M. Szewczuk, F. Zulkernine
We present a deep learning model to apply computer vision to detect prostate cancer spheroid cultures and calculate their volume. Multicellular tumour spheroids, or simply spheroids, represent a three-dimensional in vitro model of cancer. Spheroids are being increasingly used in drug discovery due to their superior ability to mimic the tumor microenvironment compared to monolayer cell cultures. A reduction in spheroid size in response to treatment with anticancer agents is indicative of the success of the therapy. As such, accurate spheroid detection and volume estimation is critical in assays involving spheroids. Automating spheroid detection and measurement reduces manual labor, laboratory costs, and research time. Our system is implemented using Darkflow YOLOv2, a single-phase object detector, based on a twenty-four-layer convolutional neural network. The network is trained on the custom data of biochemically-generated spheroids and their corresponding images, which are then bound and detected with an F1-score of 76% and an IoU of 69%. Volume calculations applied to the identified spheroids resulted in a high volume estimation accuracy with only 3.99% average error.
我们提出了一个深度学习模型来应用计算机视觉检测前列腺癌球体培养并计算其体积。多细胞肿瘤球体,或简单的球体,代表了一个三维的体外癌症模型。由于与单层细胞培养相比,球状细胞具有更好的模拟肿瘤微环境的能力,因此越来越多地用于药物发现。对抗癌药物治疗的反应是球体大小的减少,这表明治疗成功。因此,准确的球体检测和体积估计在涉及球体的分析中是至关重要的。自动化球体检测和测量减少了人工劳动、实验室成本和研究时间。我们的系统是使用Darkflow YOLOv2实现的,这是一种基于24层卷积神经网络的单相目标检测器。该网络在生化生成的球体及其相应图像的定制数据上进行训练,然后将其绑定并检测,f1得分为76%,IoU为69%。对所识别的球体进行体积计算,得到了较高的体积估计精度,平均误差仅为3.99%。
{"title":"Computer Vision for Detecting and Measuring Multicellular Tumor Shperoids of Prostate Cancer","authors":"Alex Wojaczek, Regina-Veronicka Kalaydina, Mohammed Gasmallah, M. Szewczuk, F. Zulkernine","doi":"10.1109/SSCI44817.2019.9002908","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002908","url":null,"abstract":"We present a deep learning model to apply computer vision to detect prostate cancer spheroid cultures and calculate their volume. Multicellular tumour spheroids, or simply spheroids, represent a three-dimensional in vitro model of cancer. Spheroids are being increasingly used in drug discovery due to their superior ability to mimic the tumor microenvironment compared to monolayer cell cultures. A reduction in spheroid size in response to treatment with anticancer agents is indicative of the success of the therapy. As such, accurate spheroid detection and volume estimation is critical in assays involving spheroids. Automating spheroid detection and measurement reduces manual labor, laboratory costs, and research time. Our system is implemented using Darkflow YOLOv2, a single-phase object detector, based on a twenty-four-layer convolutional neural network. The network is trained on the custom data of biochemically-generated spheroids and their corresponding images, which are then bound and detected with an F1-score of 76% and an IoU of 69%. Volume calculations applied to the identified spheroids resulted in a high volume estimation accuracy with only 3.99% average error.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"54 1","pages":"563-569"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80425188","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
Efficient Methods for Agile Earth Observation Satellite Scheduling 敏捷对地观测卫星调度的有效方法
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003056
Xiaoyu Zhao, Zhaohui Wang, Jimin Lv, Yingwu Chen
Agile Earth observation satellite (AEOS) scheduling is a complex optimization problem with longer visible time and Time-dependent transition time constraint. We address one method to transform the AEOS scheduling problem to Maximum weight independent set (MWIS) problem to reduce the difficulty of modeling and solving. We also propose reduction and decomposition strategy to reduce the scale of the problem. Experiments proved IP model established by these methods can get optimal solution of the problem, whose size is larger than previous research. Additional, iterated local search hybrid with Variable neighborhood search (VNS-ILS) is designed to solve MWIS problem from AEOS scheduling. The proposed algorithm almost increases all solution quality than ALNS and ALNS-TS adopting in previous research for AEOS scheduling.
敏捷对地观测卫星(AEOS)调度是一个具有较长可见时间和时变过渡时间约束的复杂优化问题。提出了一种将AEOS调度问题转化为最大权独立集问题的方法,以降低建模和求解的难度。我们还提出了减少和分解的策略,以减少问题的规模。实验证明,利用这些方法建立的IP模型可以得到问题的最优解,且问题的规模比以往的研究要大。此外,设计了迭代局部搜索与可变邻域搜索(VNS-ILS)的混合算法来解决AEOS调度中的MWIS问题。与以往研究中采用的ALNS和ALNS- ts算法相比,本文提出的算法几乎提高了AEOS调度的所有解的质量。
{"title":"Efficient Methods for Agile Earth Observation Satellite Scheduling","authors":"Xiaoyu Zhao, Zhaohui Wang, Jimin Lv, Yingwu Chen","doi":"10.1109/SSCI44817.2019.9003056","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003056","url":null,"abstract":"Agile Earth observation satellite (AEOS) scheduling is a complex optimization problem with longer visible time and Time-dependent transition time constraint. We address one method to transform the AEOS scheduling problem to Maximum weight independent set (MWIS) problem to reduce the difficulty of modeling and solving. We also propose reduction and decomposition strategy to reduce the scale of the problem. Experiments proved IP model established by these methods can get optimal solution of the problem, whose size is larger than previous research. Additional, iterated local search hybrid with Variable neighborhood search (VNS-ILS) is designed to solve MWIS problem from AEOS scheduling. The proposed algorithm almost increases all solution quality than ALNS and ALNS-TS adopting in previous research for AEOS scheduling.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"53 1","pages":"3158-3164"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81260786","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
A Study of the Naïve Objective Space Normalization Method in MOEA/D Naïve MOEA/D目标空间归一化方法研究
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002938
Linjun He, Yang Nan, Ke Shang, H. Ishibuchi
Complex Pareto fronts with objectives in different scales usually appear in real-world multi-objective optimization problems. So as to treat different objectives equally, the naïve normalization method is frequently used due to its simplicity in calculating the estimated ideal and nadir points (i.e., without generating a hyperplane). By directly making use of information from the obtained solutions, the estimated ideal point and the estimated nadir point are obtained. However, the naïve normalization method has rarely been investigated. Moreover, its formulation is often different in each study in the literature. In this paper, we first show four different formulations of the naïve normalization. They are based on different estimation mechanisms of the ideal point and the nadir point. Next we investigate the effect of each formulation on the performance of MOEA/D. Our results show that the search behavior of MOEA/D is significantly impacted due to the choice of a formulation of the naïve normalization method. Finally we suggest the most effective formulation of the naïve normalization method for MOEA/D.
在现实世界的多目标优化问题中,经常会出现不同尺度目标的复杂Pareto前沿。为了平等对待不同的目标,我们经常使用naïve归一化方法,因为它计算估计的理想点和最低点很简单(即不产生超平面)。直接利用得到的解的信息,得到了估计的理想点和估计的最低点。然而,naïve归一化方法很少被研究。此外,它的表述往往是不同的,在每一个研究文献。在本文中,我们首先展示了naïve归一化的四种不同形式。它们基于理想点和最低点的不同估计机制。接下来,我们研究了每种配方对MOEA/D性能的影响。我们的研究结果表明,由于选择naïve归一化方法的公式,MOEA/D的搜索行为受到显著影响。最后提出了最有效的naïve MOEA/D归一化方法。
{"title":"A Study of the Naïve Objective Space Normalization Method in MOEA/D","authors":"Linjun He, Yang Nan, Ke Shang, H. Ishibuchi","doi":"10.1109/SSCI44817.2019.9002938","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002938","url":null,"abstract":"Complex Pareto fronts with objectives in different scales usually appear in real-world multi-objective optimization problems. So as to treat different objectives equally, the naïve normalization method is frequently used due to its simplicity in calculating the estimated ideal and nadir points (i.e., without generating a hyperplane). By directly making use of information from the obtained solutions, the estimated ideal point and the estimated nadir point are obtained. However, the naïve normalization method has rarely been investigated. Moreover, its formulation is often different in each study in the literature. In this paper, we first show four different formulations of the naïve normalization. They are based on different estimation mechanisms of the ideal point and the nadir point. Next we investigate the effect of each formulation on the performance of MOEA/D. Our results show that the search behavior of MOEA/D is significantly impacted due to the choice of a formulation of the naïve normalization method. Finally we suggest the most effective formulation of the naïve normalization method for MOEA/D.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"60 1","pages":"1834-1840"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87637265","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: 8
A Bayesian Discrete Optimization Algorithm for Permutation Based Combinatorial Problems 基于排列组合问题的贝叶斯离散优化算法
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002675
Jianming Zhang, Xifan Yao, Min Liu, Yan Wang
Bayesian optimization (BO) is a versatile and robust global optimization method under uncertainty. However, most of the BO algorithms were developed for problems with only continuous variables. For practical engineering optimization, discrete variables are also prevalent. BO methods based on Gaussian process (GP) surrogates also suffers from the curse-of-dimensionality problem. To address these challenges, in this paper, a Bayesian discrete optimization algorithm is introduced to solve permutation-based combinatorial problems. A new kernel function is developed based on position distances for permutation. To improve the efficiency and scalability of the algorithm, a sparse GP model based on inducing points is further developed, where the simulated annealing algorithm is applied to select inducing points. The new algorithm is demonstrated and tested with the production scheduling problem for additive manufacturing. Experimental results show that the proposed algorithm can find a better solution with limited evaluations than state-of-the-art algorithms.
贝叶斯优化是一种通用的、鲁棒的不确定全局优化方法。然而,大多数BO算法都是针对只有连续变量的问题而开发的。对于实际的工程优化,离散变量也很普遍。基于高斯过程(GP)替代物的BO方法也存在维数诅咒问题。为了解决这些问题,本文引入了贝叶斯离散优化算法来解决基于排列的组合问题。提出了一种基于位置距离的置换核函数。为了提高算法的效率和可扩展性,进一步发展了基于诱导点的稀疏GP模型,其中采用模拟退火算法选择诱导点。以增材制造生产调度问题为例,对新算法进行了验证。实验结果表明,与现有算法相比,该算法可以在有限的评估条件下找到更好的解。
{"title":"A Bayesian Discrete Optimization Algorithm for Permutation Based Combinatorial Problems","authors":"Jianming Zhang, Xifan Yao, Min Liu, Yan Wang","doi":"10.1109/SSCI44817.2019.9002675","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002675","url":null,"abstract":"Bayesian optimization (BO) is a versatile and robust global optimization method under uncertainty. However, most of the BO algorithms were developed for problems with only continuous variables. For practical engineering optimization, discrete variables are also prevalent. BO methods based on Gaussian process (GP) surrogates also suffers from the curse-of-dimensionality problem. To address these challenges, in this paper, a Bayesian discrete optimization algorithm is introduced to solve permutation-based combinatorial problems. A new kernel function is developed based on position distances for permutation. To improve the efficiency and scalability of the algorithm, a sparse GP model based on inducing points is further developed, where the simulated annealing algorithm is applied to select inducing points. The new algorithm is demonstrated and tested with the production scheduling problem for additive manufacturing. Experimental results show that the proposed algorithm can find a better solution with limited evaluations than state-of-the-art algorithms.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"35 1","pages":"874-881"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89002294","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
Distributed Collision-Avoidance Formation Control: A Velocity Obstacle-Based Approach 分布式避碰编队控制:一种基于速度障碍的方法
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003159
Yifan Hu, Han Yu, Yang Zhong, Yuezu Lv
This paper proposes a discrete-time algorithm for formation control of multi-agent systems with collision avoidance, where the agents’ velocity and steering constraints are also considered. To ensure the collision avoidance, the velocity obstacle and reciprocal velocity obstacle methods are introduced to modify the distributed formation algorithm, where each agent uses velocity obstacle method to avoid collision with obstacles, and reciprocal velocity obstacle method to avoid collision with other agents. In this sense, each agent has the ability to pass through complex obstacle environments by autonomously changing prefer velocity. Simulation results show that the proposed algorithm can achieve formation task and meanwhile guarantee collision avoidance.
本文提出了一种考虑多智能体速度约束和转向约束的离散时间避碰编队控制算法。为了避免碰撞,引入速度障碍法和反向速度障碍法对分布式编队算法进行改进,每个agent使用速度障碍法避免与障碍物碰撞,使用反向速度障碍法避免与其他agent碰撞。从这个意义上说,每个智能体都有能力通过自主改变偏好速度来通过复杂的障碍环境。仿真结果表明,该算法在实现编队任务的同时保证了编队的避碰。
{"title":"Distributed Collision-Avoidance Formation Control: A Velocity Obstacle-Based Approach","authors":"Yifan Hu, Han Yu, Yang Zhong, Yuezu Lv","doi":"10.1109/SSCI44817.2019.9003159","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003159","url":null,"abstract":"This paper proposes a discrete-time algorithm for formation control of multi-agent systems with collision avoidance, where the agents’ velocity and steering constraints are also considered. To ensure the collision avoidance, the velocity obstacle and reciprocal velocity obstacle methods are introduced to modify the distributed formation algorithm, where each agent uses velocity obstacle method to avoid collision with obstacles, and reciprocal velocity obstacle method to avoid collision with other agents. In this sense, each agent has the ability to pass through complex obstacle environments by autonomously changing prefer velocity. Simulation results show that the proposed algorithm can achieve formation task and meanwhile guarantee collision avoidance.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"27 1","pages":"1994-2000"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89057953","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
Semi-supervised Image Segmentation Based on K- means Algorithm and Random Walk 基于K均值算法和随机漫步的半监督图像分割
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003175
Cai Xiumei, Bian Jingwei, Wang Yan, Cui Qiaoqiao
Semi-supervised image segmentation is a process of classifying unlabeled pixels using known labeling information. In order to realize image segmentation, solve the problem of setting a large number of seed points in the random walk algorithm, and solve the local optimization problem in the K- means algorithm, this paper proposes a semi-supervised image segmentation algorithm based on the K-means algorithm and random walk. Firstly, the K-means algorithm is used for clustering to determine the clustering center, then, the transfer probability from each unlabeled pixel to the seed point is calculated based on the random walk algorithm, and the image segmentation is completed according to the transfer probability. It can be seen from the experimental results that the segmentation accuracy is greatly improved and the effectiveness of this paper is verified.
半监督图像分割是利用已知的标记信息对未标记像素进行分类的过程。为了实现图像分割,解决随机漫步算法中设置大量种子点的问题,解决K-means算法中的局部优化问题,本文提出了一种基于K-means算法和随机漫步的半监督图像分割算法。首先使用K-means算法进行聚类,确定聚类中心,然后基于随机游走算法计算每个未标记像素到种子点的转移概率,并根据转移概率完成图像分割。从实验结果可以看出,分割精度得到了很大的提高,验证了本文的有效性。
{"title":"Semi-supervised Image Segmentation Based on K- means Algorithm and Random Walk","authors":"Cai Xiumei, Bian Jingwei, Wang Yan, Cui Qiaoqiao","doi":"10.1109/SSCI44817.2019.9003175","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003175","url":null,"abstract":"Semi-supervised image segmentation is a process of classifying unlabeled pixels using known labeling information. In order to realize image segmentation, solve the problem of setting a large number of seed points in the random walk algorithm, and solve the local optimization problem in the K- means algorithm, this paper proposes a semi-supervised image segmentation algorithm based on the K-means algorithm and random walk. Firstly, the K-means algorithm is used for clustering to determine the clustering center, then, the transfer probability from each unlabeled pixel to the seed point is calculated based on the random walk algorithm, and the image segmentation is completed according to the transfer probability. It can be seen from the experimental results that the segmentation accuracy is greatly improved and the effectiveness of this paper is verified.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"61 12","pages":"2853-2856"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91439748","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
A Fast Algorithm for HEVC Screen Content Coding HEVC屏幕内容编码的快速算法
Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002825
H. Tang, Y. Duan, L. Sun, Yi ran Li
As a high efficiency video coding (HEVC) extension, screen content coding (SCC) has a significant effect in the compressed screen content, but causes a problem encoder calculates a higher complexity. This paper proposes a fast decision method based on HEVC screen content coding. The image content is divided into a natural coding unit (NCU) and a screen coding unit (SCU). The image gradients of different properties are different, and the gini impurity classification gradient value is used to speed up the encoding speed. The experimental results show that under the frame configuration of HM-15.0 SCM-2.0, the method can save about 41.4% of the encoder time.
屏幕内容编码(SCC)作为一种高效的视频编码(HEVC)扩展,在压缩屏幕内容方面效果显著,但造成编码器计算复杂度较高的问题。提出了一种基于HEVC屏幕内容编码的快速决策方法。图像内容分为自然编码单元(NCU)和屏幕编码单元(SCU)。不同属性的图像梯度不同,采用基尼杂质分类梯度值来加快编码速度。实验结果表明,在HM-15.0 SCM-2.0帧配置下,该方法可节省约41.4%的编码器时间。
{"title":"A Fast Algorithm for HEVC Screen Content Coding","authors":"H. Tang, Y. Duan, L. Sun, Yi ran Li","doi":"10.1109/SSCI44817.2019.9002825","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002825","url":null,"abstract":"As a high efficiency video coding (HEVC) extension, screen content coding (SCC) has a significant effect in the compressed screen content, but causes a problem encoder calculates a higher complexity. This paper proposes a fast decision method based on HEVC screen content coding. The image content is divided into a natural coding unit (NCU) and a screen coding unit (SCU). The image gradients of different properties are different, and the gini impurity classification gradient value is used to speed up the encoding speed. The experimental results show that under the frame configuration of HM-15.0 SCM-2.0, the method can save about 41.4% of the encoder time.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"4 1","pages":"2921-2925"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83722888","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
期刊
2019 IEEE Symposium Series on Computational Intelligence (SSCI)
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1