检索结果-内蒙古大学图书馆

Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

引用

NATURAL COMPUTING 2022年第2期21卷 187-217页

作者： Harada, Tomohiro Kaidan, Misaki Thawonmas, Ruck Tokyo Metropolitan Univ Fac Syst Design 6-6 Asahigaoka Hino Tokyo Japan Ritsumeikan Univ Grad Sch Informat Sci & Engn 1-1-1 Noji Higashi Kusatsu Shiga Japan Ritsumeikan Univ Coll Informat Sci & Engn 1-1-1 Noji Higashi Kusatsu Shiga Japan

This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

关键词： evolutionary computation Extreme learning machine Multi-objective optimization Parallelization Surrogate model

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes

引用

IEEE ACCESS 2022年 10卷 107095-107115页

作者： Nouhi, Behnaz Khodadadi, Nima Azizi, Mahdi Talatahari, Siamak Gandomi, Amir H. Univ Tabriz Dept Math Sci Tabriz 51666 Iran Florida Int Univ Coll Engn & Comp Dept Civil & Environm Engn Miami FL 33199 USA Univ Tabriz Dept Civil Engn Tabriz 51666 Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary RMIT Univ Sch Engn Melbourne Vic 3001 Australia

Optimization is a process of decision-making in which some iterative procedures are conducted to maximize or minimize a predefined objective function representing the overall behavior of a considered system problem. Most of the time, one specific function cannot represent the overall behavior of a system with particular levels of complexity, so the multiple objective functions should be determined for this purpose which requires an algorithm with adaptability to this situation. Multi-objective optimization is a process of decision making in which maximization or minimization of multiple objective functions is considered for reaching the acceptable levels of performance for the considered system problem. In this paper, the multi-objective version of the Material Generation Algorithm (MGA) is proposed as MOMGA, one of the recently developed metaheuristic algorithms for single-objective optimization. To evaluate the overall performance of the MOMGA, the benchmark multi-objective optimization problems of the Competitions on evolutionary computation (CEC) are considered alongside the real-world engineering problems. Based on the results, the MOMGA is capable of providing very acceptable results in dealing with multi-objective optimization problems.

关键词： Metaheuristics evolutionary computation Chemical reactions Chemical compounds Heuristic algorithms Algorithm design and analysis Machine learning algorithms Material generation algorithm multi-objective optimization real-world engineering problems competitions on evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Discrete bacterial foraging optimization for community detection in networks

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 128卷 192-204页

作者： Yang, Bo Huang, Xuelin Cheng, Weizheng Huang, Tao Li, Xu Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

An essential mesoscopic concept in network analysis is that of community structure. However, conventional nature-inspired optimization algorithms encounter serious challenges and difficulties when used directly to seek communities in networks, due to the large amount of data and the NP hard combinatorial nature of the problem. Thus in this paper, we introduce a novel bacterial foraging optimization approach to uncovering community structure in networks. Instead of using the original bacterial foraging designed traditionally for continuous optimization, the problem of community detection is exquisitely embedded into a redefined discrete framework. The evolutionary principles for the bacterial foraging are developed from a topological perspective. Furthermore, two specific local updating rules, namely the greedy strategy and the stochastic strategy, are designed to steer the swarm of bacteria to the favored regions. The extensive experimental results on both synthetic and real-world networks indicate that the proposed approach outperforms the baseline algorithms and can achieve a high accuracy on the uncovered community structure. The integration of the proposed approach into the analysis of power grids and its explicit utility are also discussed in detail, showing that our method has high accuracy and practicability. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Networks Community detection Bacterial foraging optimization evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Force-Based Algorithm for Motion Planning of Large Agent

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 654-665页

作者： Semnani, Samaneh Hosseini de Ruiter, Anton H. J. Liu, Hugh H. T. Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Ryerson Univ Aerosp Engn Dept Toronto ON M5B 2K3 Canada Univ Toronto Fac Appl Sci & Engn Toronto ON M3H 5T6 Canada

This article presents a distributed, efficient, scalable, and real-time motion planning algorithm for a large group of agents moving in 2-D or 3-D spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: 1) collision avoidance and 2) navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm can find collision-free motions with lower transition time, free from velocity state information of neighboring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2-D and 3-D benchmark simulation scenarios, with results outperforming existing methods.

关键词： Planning Heuristic algorithms Trajectory Real-time systems Navigation computational complexity Force Collision avoidance distributed algorithms evolutionary computation flocking algorithm motion planning multiagent systems trajectory optimization

来源：评论

学校读者我要写书评

暂无评论

evolutionary Multitasking AUC Optimization [Research Frontier]

引用

IEEE computationAL INTELLIGENCE MAGAZINE 2022年第2期17卷 67-82页

作者： Chao, Wang Wu, Kai Liu, Jing Xidian Univ Xian Peoples R China

Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimization is still an open issue due to its pairwise learning style. Maximizing AUC in the large-scale dataset can be considered as a non-convex and expensive problem. Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task. This paper develops an evolutionary multitasking framework (termed EMTAUC) to make full use of information among the constructed cheap and expensive tasks to obtain higher performance. In EMTAUC, one mission is to optimize AUC from the sampled dataset, and the other is to maximize AUC from the original dataset. Moreover, due to the cheap task containing limited knowledge, a strategy for dynamically adjusting the data structure of inexpensive tasks is proposed to introduce more knowledge into the multitasking AUC optimization environment. The performance of the proposed method is evaluated on a series of binary classification datasets. The experimental results demonstrate that EMTAUC is highly competitive to single task methods and online methods. Supplementary materials and source code implementation of EMTAUC can be accessed at https://***/xiaofangxd/EMTAUC.

关键词： Codes Optimization methods Receivers Machine learning Multitasking Data structures Proposals evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

A Surrogate-Assisted evolutionary Feature Selection Algorithm With Parallel Random Grouping for High-Dimensional Classification

引用

IEEE TRANSACTIONS ON evolutionary computation 2022年第5期26卷 1087-1101页

作者： Liu, Shulei Wang, Handing Peng, Wei Yao, Wen Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Chinese Acad Mil Sci Def Innovat Inst Beijing 100071 Peoples R China Xidian Univ Collaborat Innovat Ctr Quantum Informat Shaanxi P Xian 710071 Peoples R China

Various evolutionary algorithms (EAs) have been proposed to address feature selection (FS) problems, in which a large number of fitness evaluations are needed. With the rapid growth of data scales, the fitness evaluation becomes time consuming, which makes FS problems expensive optimization problems. Surrogate-assisted EAs (SAEAs) have been widely used to solve expensive optimization problems. However, the SAEAs still face difficulties in solving expensive FS problems due to their high-dimensional discrete decision variables. To address this issue, we propose an SAEA with parallel random grouping for expensive FS problems, in which three main components consist. First, a constraint-based sampling strategy is proposed, which considers the influence of the constraint boundary and the number of selected features. Second, a high-dimensional FS problem is randomly divided into several low-dimensional subproblems. Surrogate models are then constructed in these low-dimensional decision spaces. After that, all the subproblems are optimized in parallel. The process of random grouping and parallel optimization continues until the termination condition is met. Finally, a final solution is chosen from the best solution in the historical search and the best solution in the last population using a random, distance-, or voting-based method. Experimental results show that the proposed algorithm generally outperforms traditional, ensemble, and evolutionary FS methods on 14 datasets with up to 10 000 features, especially when the required number of real fitness evaluations is limited.

关键词： Optimization computational modeling Search problems Feature extraction evolutionary computation Classification algorithms Training High-dimensional feature selection~(FS) random grouping sampling strategy surrogate-assisted EA~(SAEA)

来源：评论

学校读者我要写书评

暂无评论

Clustering Ensemble Based on Hybrid Multiview Clustering

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 6518-6530页

作者： Yu, Zhiwen Wang, Daxing Meng, Xian-Bing Chen, C. L. Philip Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Macau Fac Sci & Technol Macau 99999 Peoples R China

As an effective method for clustering applications, the clustering ensemble algorithm integrates different clustering solutions into a final one, thus improving the clustering efficiency. The key to designing the clustering ensemble algorithm is to improve the diversities of base learners and optimize the ensemble strategies. To address these problems, we propose a clustering ensemble framework that consists of three parts. First, three view transformation methods, including random principal component analysis, random nearest neighbor, and modified fuzzy extension model, are used as base learners to learn different clustering views. A random transformation and hybrid multiview learning-based clustering ensemble method (RTHMC) is then designed to synthesize the multiview clustering results. Second, a new random subspace transformation is integrated into RTHMC to enhance its performance. Finally, a view-based self-evolutionary strategy is developed to further improve the proposed method by optimizing random subspace sets. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method for clustering different kinds of data.

关键词： Clustering algorithms Kernel Machine learning algorithms Research and development Machine learning Fuses evolutionary computation Cluster ensemble ensemble learning multiview clustering random subspace transformation

来源：评论

学校读者我要写书评

暂无评论

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

引用

IET RENEWABLE POWER GENERATION 2022年第8期16卷 1535-1561页

作者： Yu, Sudan Chen, Zhiqing Heidari, Ali Asghar Zhou, Wei Chen, Huiling Xiao, Lei Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Elect & Elect Engn Wenzhou Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran

In this paper, an efficient sine cosine differential gradient-based optimization method is proposed for identifying unknown parameters of photovoltaic models. In the simulation, parameter identification is formulated as an objective function to be minimized based on the error between the estimated and experimental data. Based on the original gradient-based optimization method, the proposed method combines the sine cosine method and the mutation crossover of the differential evolution algorithm. Specifically, the crossover operator enables the algorithm to avoid local optima;and meanwhile, the sine cosine strategy encourages the new individual to calculate the worst position. The simulation results demonstrate that the new optimization method can achieve the minimal root mean square error and obtain better optima relative to other algorithms in different photovoltaic cells. Therefore, the proposed optimization method has great potential to be used for estimating photovoltaic model parameters.

关键词： optimisation original gradient-based optimization method differential evolution algorithm photovoltaic models search problems gradient methods parameter identification unknown parameters different photovoltaic cells efficient sine cosine differential gradient-based optimization method photovoltaic model parameters sine cosine method sine cosine strategy evolutionary computation parameter estimation mean square error methods genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

MO4: A Many-Objective evolutionary Algorithm for Protein Structure Prediction

引用

IEEE TRANSACTIONS ON evolutionary computation 2022年第3期26卷 417-430页

作者： Lei, Zhenyu Gao, Shangce Zhang, Zhiming Zhou, MengChu Cheng, Jiujun Univ Toyama Fac Engn Toyama 9308555 Japan New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 200092 Peoples R China

Protein structure prediction (PSP) problems are a major biocomputing challenge, owing to its scientific intrinsic that assists researchers to understand the relationship between amino acid sequences and protein structures, and to study the function of proteins. Although computational resources increased substantially over the last decade, a complete solution to PSP problems by computational methods has not yet been obtained. Using only one energy function is insufficient to characterize proteins because of their complexity. Diverse protein energy functions and evolutionary computation algorithms have been extensively studied to assist in the prediction of protein structures in different ways. Such algorithms are able to provide a better protein with less computational resources requirement than deep learning methods. For the first time, this study proposes a many-objective PSP (MaOPSP) problem with four types of objectives to alleviate the impact of imprecise energy functions for predicting protein structures. A many-objective evolutionary algorithm (MaOEA) is utilized to solve MaOPSP. The proposed method is compared with existing methods by examining 34 proteins. An analysis of the objectives demonstrates that our generated conformations are more reasonable than those generated by single/multiobjective optimization methods. Experimental results indicate that solving a PSP problem as an MaOPSP problem with four objectives yields better PSPs, in terms of both accuracy and efficiency. The source code of the proposed method can be found at https://***/***.

关键词： Proteins Force evolutionary computation computational modeling Search problems Frequency modulation Prediction algorithms Deep learning evolutionary computation free modeling many-objective optimization evolutionary algorithm (MaOEA) protein structure prediction (PSP)

来源：评论

学校读者我要写书评

暂无评论

Competition-Driven Multimodal Multiobjective Optimization and Its Application to Feature Selection for Credit Card Fraud Detection

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第12期52卷 7845-7857页

作者： Han, Shoufei Zhu, Kun Zhou, MengChu Cai, Xinye Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 211106 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China

Feature selection has been considered as an effective method to solve imbalanced classification problems. It can be formulated as a multiobjective optimization problem (MOP) aiming to find a small feature subset while achieving a high classification accuracy. With traditional MOP, the focus is on deriving an optimal solution (i.e., a feature subset), while ignoring the diversity in solution space (e.g., there could exist multiple feature subsets achieving the same accuracy). Providing more options for feature selection would be beneficial since some features can be more difficult to obtain than others. In this work, we treat feature selection as a multimodal MOP (MMOP) whose goals are to find an excellent Pareto front in objective space and as many equivalent Pareto optimal solutions (feature subsets) as possible in feature space. Note that though several multimodal multiobjective evolutionary algorithms (MMEAs) have been proposed, their use of a convergence-first selection criterion could cause the loss of solution diversity in an objective and feature space. To address the issue, a novel competition-driven mechanism is designed to assist the existing multimodal MMEAs in locating more equivalent feature subsets and a desired Pareto front. The effectiveness of the proposed mechanism is first verified on all 22 MMOPs from CEC2019. Then, the proposed method is applied to feature selection in imbalanced classification problems and a real-world application, i.e., credit card fraud detection. Experimental results show that the proposed mechanism can not only provide more equivalent feature subsets but also improve classification accuracy.

关键词： Feature extraction Optimization evolutionary computation Credit cards Convergence Search problems Task analysis Competition mechanism (CM) credit card fraud detection feature selection imbalanced classification machine learning multimodal multiobjective evolutionary algorithms (MMEAs) multimodal MOPs (MMOPs)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：