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IEEE Radar Conference (RADAR)

作者： Stringer, Jeremy Lamont, Gary Akers, Geoffrey USAF Dept Elect & Comp Engn Inst Technol Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781467306584

Solving complex real world multi-objective Optimization problems is the forte of multi-objective evolutionary algorithms (MOEA). Such algorithms have been part of many scientific and engineering endeavors. This study applies the NSGA-II and SPEA2 MOEAs to the radar phase coded waveform design problem. The MOEAs are used to generate a series of radar waveform phase codes that have excellent range resolution and Doppler resolution capabilities. The study compares the ability of NSGA-II and SPEA2 to continually evolve (phase code) solutions on the Pareto front for the problem while maintaining a diversity of solutions (phase codes). Results demonstrate that for the radar phase code problem NSGA-II provides a more diverse population of acceptable solutions and therefore a greater number of different viable phase codes when compared to the solutions provided by SPEA2

关键词： multi-objective evolutionary algorithm radar phase coding Distance resolution design problems waveforms

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multi-objective evolutionary algorithm Based Optimization of Neural Network Ensemble Classifier 8

Multi-Objective Evolutionary Algorithm Based Optimization of...

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8th International Conference on Signal Processing and Communication Systems, (ICSPCS)

作者： Chiu, Chien-Yuan Verma, Brijesh Cent Queensland Univ Brisbane Qld Australia

ISBN: (纸本)9781479952557

The purpose of this paper is to investigate a multi-objective evolutionary algorithm (MOEA) for optimizing neural ensemble classifiers. This paper provides an automatic procedure based on MOEA to identify the best accuracy and diversity. A MOEA is used to search for the combination of layers and clusters in ensemble classifiers to obtain the non-dominated set of accuracy and diversity. The experiments were conducted on UCI machine learning benchmark datasets using the MOEA and also single objective evolutionary algorithms. The detailed results and analysis show that MOEA has improved the performance of ensemble classifier and obtained better accuracy compared to recently published approaches.

关键词： multi-objective evolutionary algorithm Neural ensemble classifiers evolutionary algorithms optimization

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multi-objective evolutionary algorithm for Oil Spill Detection from COSMO-SkeyMed Satellite

Multi-Objective Evolutionary Algorithm for Oil Spill Detecti...

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14th International Conference on Computational Science and Its Applications (ICCSA)

作者： Marghany, Maged Univ Teknol Malaysia Inst Geospatial Sci & Technol INSTeG Skudai 81310 Johor Bahru Malaysia

ISBN: (纸本)9783319091532

This study has demonstrated a design tool for oil spill detection in COSMO-SkyMed satellite data using multi-objective evolutionary algorithm which based on Pareto optimal solutions. The COSMO-SkyMed along the Gulf of Thailand is involved in this study. The study also shows that multi-objective evolutionary algorithm provides an accurate pattern of oil slick in COSMO-SkyMed data. This shown by 96% for oil spill, 1% look-alike and 3% for sea roughness using the receiver -operational characteristics (ROC) curve. The MOGA also shows excellent performance in COSMO-SkyMed data. In conclusion, multi-objective evolutionary algorithm can be used as an automatic detection tool for oil spill in COSMO-SkyMed satellite data.

关键词： multi-objective evolutionary algorithm COSMO-SkyMed oil spill Pareto optimal solutions Automatic detection

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multi-objective evolutionary algorithm Based on Correlativity and Its Application

Multi-objective Evolutionary Algorithm Based on Correlativit...

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7th World Congress on Intelligent Control and Automation

作者： Li, Junfeng Dai, Wenzhan Zhejiang Sci Tech Univ Dept Automat Control Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781424421138

In this paper, based on correlativity theory, a kind of multi-objective evolutionary algorithm is put forward. First, the best solution of every objective among the mufti-objectives is obtained and they are regarded on as the referenced vector. Second, the correlativity index between every individual and the referenced vector is solved and the correlativity index is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.

关键词： multi-objective evolutionary algorithm Degree of grey incidence Included angle cosine

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multi-objective evolutionary algorithm Based on the Fuzzy Similarity Measure

Multi-objective Evolutionary Algorithm Based on the Fuzzy Si...

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International Conference on Advanced Measurement and Test (AMT 2010)

作者： Li, Junfeng Dai, Wenzhan Wang, Huijiao Zhejiang Sci Tech Univ Dept Automat Control Hangzhou Zhejiang Peoples R China

ISBN: (纸本)0878492712

evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. This paper proposes a novel multi-objective evolutionary algorithm based on the fuzzy similarity measure. First, the best solution of every objective among the multi-objectives is obtained and they are regarded on as the referenced vector. Second, the fuzzy similarity measure between every individual and the referenced vector is solved and the fuzzy similarity measure is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.

关键词： multi-objective evolutionary algorithm Fuzzy similarity measure Genetic algorithm

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multi-objective evolutionary algorithm FOR IMAGE SEGMENTATION

MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR IMAGE SEGMENTATIO...

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IEEE SoutheastCon Conference

作者： Abeysinghe, Wajira Wong, Michael Hung, Chih-Cheng Bechikh, Slim Kennesaw State Univ Dept Comp Sci Marietta GA 30060 USA Anyang Normal Univ Anyang Henan Peoples R China Univ Tunis Comp Sci Dept ISG Campus Tunis Tunisia

ISBN: (纸本)9781728101378

Clustering is an unsupervised learning technique commonly used for image segmentation. As the outcome of most clustering algorithms is heavily dependent on the initial cluster centers, it is necessary to consider optimization during the process of segmentation. The multi-objective evolutionary algorithm (MOEA) was used for optimization in this study, to find optimal cluster centers. It is important to note that the effectiveness of MOEA is dependent upon the selection of objective functions. Two objectives were considered;namely, the minimization of intra-cluster compactness and the maximization of inter-cluster separation to determine the optimal initial cluster centers. Xie-Beni index (XBI) was used to measure the compactness and separation of cluster centers while the Average Inter-Cluster Separation (AIS) measure ensures the minimal overlapping of clusters. The MOEA will generate a set of non-dominated solutions. The Davies-Bouldin Index (DBI) is then employed to determine the most optimal solution for the cluster centers. Experimental results demonstrate that this method of image segmentation performs better than single-objective optimization (SOO)and Possibilistic Clustering algorithm (PCA).

关键词： possibilistic clustering algorithm multi-objective evolutionary algorithm pareto-optimal cluster validity measures image segmentation

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A multi-objective evolutionary algorithm for Promoting the Emergence of Cooperation and Controllable Robustness on Directed Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2018年第2期5卷 92-100页

作者： Wang, Shuai Liu, Jing Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

The directedness of links is of significance in complex networks, and much attention has been paid to study the dynamics of directed networks recently. In networked systems, where the emergence of cooperation and robustness are two hot issues in recent decades. Previous studies have indicated that the structures for promoting these two properties are opposite, which also reveals the great impact of structures on the functionalities of networks. Moreover, several realistic problems also reflect the importance of simultaneously promoting the robustness and cooperation maintaining ability on directed networks, however, few studies have focused on solving this urgent problem. Therefore, in this paper, concentrating on optimizing the cooperation maintaining ability together with controllable robustness on directed networks, we first model this issue as a multi-objective optimization problem, and then a multi-objective evolutionary algorithm, labeled as MOEA-Net(cc), has been devised to solve this problem. In the experiments, the performance of MOEA-Net(cc) is validated on both synthetic and real networks, and the results show that MOEA-Net(cc) can not only achieve balanced optimal results without changing degree distribution of networks;but also create diverse Pareto fronts, which provide various potential candidates for decision makers to deal with social and economic dilemmas.

关键词： Complex network cooperation multi-objective optimization robustness multi-objective evolutionary algorithm

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Optimal reservoir flood operation using a decomposition-based multi-objective evolutionary algorithm

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ENGINEERING OPTIMIZATION 2019年第1期51卷 42-62页

作者： Zhang, Xiao Luo, Jungang Sun, Xiaomei Xie, Jiancang Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian Shaanxi Peoples R China

Reservoir flood control operation (RFCO) is a challenging optimization problem with interdependent decision variables and multiple conflicting criteria. By considering safety both upstream and downstream of the dam, a multi-objective optimization model is built for RFCO. To solve this problem, a multi-objective optimizer, the multi-objective evolutionary algorithm based on decomposition-differential evolution (MOEA/D-DE), is developed by introducing a differential evolution-inspired recombination into the algorithmic framework of the decomposition-based multi-objective optimization algorithm, which has been proven to be effective for solving complex multi-objective optimization problems. Experimental results on four typical floods at the Ankang reservoir illustrated that the suggested algorithm outperforms or performs as well as the comparison algorithms. It can significantly reduce the flood peak and also guarantee the dam's safety.

关键词： Reservoir flood control operation multi-objective evolutionary algorithm decomposition differential evolution

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Overlapping community detection in complex networks using multi-objective evolutionary algorithm

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COMPUTATIONAL & APPLIED MATHEMATICS 2017年第1期36卷 749-768页

作者： Zhao Yuxin Li Shenghong Jin Feng Shanghai Jiao Tong Univ Dept Elect Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China IBM China Res Lab 399 Ke Yuan Rd Shanghai 201203 Peoples R China

Community structure is an important topological property of complex networks, which has great significance for understanding the function and organization of networks. Generally, community detection can be formulated as a single-objective or multi-objective optimization problem. Most existing optimization-based community detection algorithms are only applicable to disjoint community structure. However, it has been shown that in most real-world networks, a node may belong to multiple communities implying overlapping community structure. In this paper, we propose a multi-objective evolutionary algorithm for identifying overlapping community structure in complex networks based on the framework of non-dominated sorting genetic algorithm. Two negatively correlated evaluation metrics of community structure, termed as negative fitness sum and unfitness sum, are adopted as the optimization objectives. In our algorithm, link-based adjacency representation of overlapping community structure and a population initialization method based on local expansion are proposed. Extensive experimental results on both synthetic and real-world networks demonstrate that the proposed algorithm is effective and promising in detecting overlapping community structure in complex networks.

关键词： Complex network Community detection Overlapping community structure Optimization problem multi-objective evolutionary algorithm

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An active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第11期20卷 19839-19857页

作者： Guo, Xiaofang Wang, Yuping Zhang, Haonan Xian Technol Univ Sch Sci Xian 710000 Peoples R China

The inverse model based multi-objective evolutionary algorithm (IM-MOEA) generates offspring by establishing probabilistic models and sampling by the model, which is a new computing schema to replace crossover in MOEAs. In this paper, an active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy (ALGM-MOEA) is proposed. To properly cope with multi-objective problems with different shapes of Pareto front (PF), a novel population guided weight vector evolution strategy is proposed to dynamically adjust search directions according to the distribution of generated PF. Moreover, in order to enhance the search efficiency and prediction accuracy, an active learning based training sample selection method is designed to build Gaussian process based inverse models, which chooses individuals with the maximum amount of information to effectively enhance the prediction accuracy of the inverse model. The experimental results demonstrate the competitiveness of the proposed ALGM-MOEA on benchmark problems with various shapes of Pareto front.

关键词： active learning multi-objective evolutionary algorithm weight vector adjustment Gaussian regression model Pareto front

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