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Hierarchical Fuzzy Logic System for Implementing Maintenance Schedules of Offshore Power Systems

IEEE TRANSACTIONS ON SMART GRID 2012年第1期3卷 3-11页

作者： Chang, C. S. Wang, Zhaoxia Yang, Fan Tan, W. W. Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore Inst High Performance Comp Dept Comp Sci Singapore 138632 Singapore

Smart grid provides the technology for modernizing electricity delivery systems by using distributed and computer-based remote sensing, control and automation, and two-way communications. Potential benefits of the technology are that the smart grid's central control will now be able to control and operate many remote power plant, optimize the overall asset utilization and operational efficiently. In this paper, we propose an innovative approach for the smart grid to handle uncertainties arising from condition monitoring and maintenance of power plant. The approach uses an adaptive maintenance advisor and a system-maintenance optimizer for designing/implementing optimized condition-based maintenance activities, and collectively handles operational variations occurring in each substation. The system-maintenance optimizer generates the initial maintenance plans for each substation with multiobjective optimization by considering only the design or average operational conditions. During operation, the substation will experience aging, control shifts, changing weather and load factors, and uncertain measurements. Residing on each host substation, the maintenance advisor will assess the adequacy of initial maintenance plans;and estimate the reliability changes caused by operational variations on the substation using a hierarchical fuzzy system. The advisor will also alert the maintenance optimizer on whether a reoptimization of its maintenance activities should be initiated for meeting the overall grid-reliability requirement. Three scenarios will be studied in this paper, which will demonstrate the ability of the proposed approach for handling operational variations occurring in an offshore substation with manageable computational complexity.

关键词： Adaptive maintenance advisor hierarchical fuzzy logic multiobjective evolutionary algorithm offshore substation smart grid system maintenance optimizer

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Multi Objective evolutionary algorithm Applied to the Optimal Power Flow Problem

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IEEE LATIN AMERICA TRANSACTIONS 2010年第3期8卷 236-244页

作者： Amorim, E. A. Hashimoto, S. H. M. Lima, F. G. M. Mantovani, J. R. S. Univ Fed Mato Grosso UFMS Depto Engn Eletr Campo Grande MS Brazil UNESP Depto Engn Eletr Ilha Solteira SP Brazil

This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique.

关键词： multiobjective evolutionary algorithm Optimal Power Flow multiobjective Optimization

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evolutionary Strategy to Enhance an RNA Design Tool Performance

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IEEE ACCESS 2024年 12卷 15582-15593页

作者： Rubio-Largo, Alvaro Escobar-Encinas, Laura Lozano-Garcia, Nuria Granado-Criado, Jose Maria Univ Extremadura Escuela Politecn Dept Comp & Telemat Syst Engn Caceres 10003 Spain Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Caceres 10003 Spain

At present, designing an RNA sequence that folds into a specific secondary structure is a problem that is not fully solved, due to its exponentially increasing complexity. To address this matter, many computational methods have been developed, but none of them has been able to completely and in an affordable time solve Eterna100, a widely recognized benchmark used to test the performance of RNA inverse folding algorithms. In previous publications we presented the m2dRNAs tool, a multiobjective evolutionary algorithm, and its extension eM2dRNAs, which added a recursive decomposition of the target structure, thus simplifying the problem. At that time they successfully improved the ability to solve the RNA inverse folding problem, but were still unable to complete the Eterna100 benchmark. Here we introduce ES+eM2dRNAs, an improvement of eM2dRNAs that optimizes the decomposition process, as a drawback in its nature was identified.A comparative study of this new tool against its predecessors and other RNA design methods was performed using the two current versions of the Eterna100 benchmark. ES+eM2dRNAs was shown to be the best in all performance indicators considered (number of structures solved, success rate, and total run time). Moreover, it is able to solve two Eterna100 structures for which none of the compared methods had ever found a solution.

关键词： Bioinformatics Sequential analysis evolutionary strategy genetic algorithm multiobjective evolutionary algorithm multiobjective optimization problem ncRNA design RNA inverse folding RNA secondary structure decomposition strategy

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scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells

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BRIEFINGS IN BIOINFORMATICS 2022年第5期23卷 bbac144-bbac144页

作者： Liu, Qiaoming Luo, Ximei Li, Jie Wang, Guohua Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Shenzhen Polytech Shenzhen Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Northeast Forestry Univ Key Lab Tree Genet & Breeding Harbin Peoples R China

The ubiquitous dropout problem in single-cell RNA sequencing technology causes a large amount of data noise in the gene expression profile. For this reason, we propose an evolutionary sparse imputation (ESI) algorithm for single-cell transcriptomes, which constructs a sparse representation model based on gene regulation relationships between cells. To solve this model, we design an optimization framework based on nondominated sorting genetics. This framework takes into account the topological relationship between cells and the variety of gene expression to iteratively search the global optimal solution, thereby learning the Pareto optimal cell-cell affinity matrix. Finally, we use the learned sparse relationship model between cells to improve data quality and reduce data noise. In simulated datasets, scESI performed significantly better than benchmark methods with various metrics. By applying scESI to real scRNA-seq datasets, we discovered scESI can not only further classify the cell types and separate cells in visualization successfully but also improve the performance in reconstructing trajectories differentiation and identifying differentially expressed genes. In addition, scESI successfully recovered the expression trends of marker genes in stem cell differentiation and can discover new cell types and putative pathways regulating biological processes.

关键词： single-cell RNA-seq imputation sparse representation multiobjective evolutionary algorithm

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A filter-based evolutionary algorithm for constrained optimization

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evolutionary COMPUTATION 2005年第3期13卷 329-352页

作者： Clevenger, L Ferguson, L Hart, WE Sandia Natl Labs Discrete Algorithms & Math Dept Albuquerque NM 87185 USA Texas Tech Inst Dept Math Lubbock TX 79409 USA

We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.

关键词： constrained optimization evolutionary pattern search convergence theory multiobjective evolutionary algorithm

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A pareto ensemble based spectral clustering framework

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COMPLEX & INTELLIGENT SYSTEMS 2021年第1期7卷 495-509页

作者： Luo, Juanjuan Ma, Huadong Zhou, Dongqing Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Northern Inst Elect Equipment China Beijing Peoples R China

Similarity matrix has a significant effect on the performance of the spectral clustering, and how to determine the neighborhood in the similarity matrix effectively is one of its main difficulties. In this paper, a "divide and conquer" strategy is proposed to model the similarity matrix construction task by adopting multiobjective evolutionary algorithm (MOEA). The whole procedure is divided into two phases, phase I aims to determine the nonzero entries of the similarity matrix, and Phase II aims to determine the value of the nonzero entries of the similarity matrix. In phase I, the main contribution is that we model the task as a biobjective dynamic optimization problem, which optimizes the diversity and the similarity at the same time. It makes each individual determine one nonzero entry for each sample, and the encoding length decreases to O(N) in contrast with the non-ensemble multiobjective spectral clustering. In addition, a specific initialization operator and diversity preservation strategy are proposed during this phase. In phase II, three ensemble strategies are designed to determine the value of the nonzero value of the similarity matrix. Furthermore, this Pareto ensemble framework is extended to semi-supervised clustering by transforming the semi-supervised information to constraints. In contrast with the previous multiobjective evolutionary-based spectral clustering algorithms, the proposed Pareto ensemble-based framework makes a balance between time cost and the clustering accuracy, which is demonstrated in the experiments section.

关键词： multiobjective evolutionary algorithm Spectral clustering Similarity matrix Pareto ensemble

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Two-Stage evolutionary Neural Architecture Search for Transfer Learning

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2021年第5期25卷 928-940页

作者： Wen, Yu-Wei Peng, Sheng-Hsuan Ting, Chuan-Kang Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 62102 Taiwan Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 30013 Taiwan

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many image classification tasks. However, training a deep CNN requires a massive amount of training data, which can be expensive or unobtainable in practical applications, such as defect inspection and medical diagnosis. Transfer learning has been developed to address this issue by transferring knowledge learned from source domains to target domains. A common approach is fine-tuning, which adapts the parameters of a trained neural network for the new target task. Nevertheless, the network architecture remains designed for the source task rather than the target task. To optimize the network architecture in transfer learning, we propose a two-stage evolutionary neural architecture search for transfer learning (EvoNAS-TL), which searches for an efficient subnetwork of the source model for the target task. EvoNAS-TL features two search stages: 1) structure search and 2) local enhancement. The former conducts a coarse-grained global search for suitable neural architectures, while the latter acts as a fine-grained local search to refine the models obtained. In this study, neural architecture search (NAS) is formulated as a multiobjective optimization problem that concurrently minimizes the prediction error and model size. The knee-guided multiobjective evolutionary algorithm, a modern multiobjective optimization approach, is employed to solve the NAS problem. In this study, several experiments are conducted to examine the effectiveness of EvoNAS-TL. The results show that applying EvoNAS-TL on VGG-16 can reduce the model size by 52%-85% and simultaneously improve the testing accuracy by 0.7%-6.9% in transferring from ImageNet to CIFAR-10 and NEU surface detection datasets. In addition, EvoNAS-TL performs comparably to or better than state-of-the-art methods on the CIFAR-10, NEU, and Office-31 datasets.

关键词： Task analysis Transfer learning Computer architecture Computational modeling Network architecture Optimization evolutionary computation Convolutional neural network (CNN) multiobjective evolutionary algorithm neural architecture search (NAS) transfer learning

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Parallel cooperative multiobjective coevolutionary algorithm for constrained multiobjective optimization problems

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APPLIED SOFT COMPUTING 2024年 153卷

作者： Harada, Tomohiro Tokyo Metropolitan Univ Factuly Syst Design 2-5036-6 Asahigaoka Hino Tokyo 1910065 Japan

The existing parallel multiobjective evolutionary computation does not perform well for constrained multiobjective optimization problems with discontinuous Pareto fronts or narrow feasible regions. This study parallelizes the state-of-the-art cooperative multiobjective coevolutionary algorithm and proposes an effective parallel evolutionary algorithm for constrained multiobjective optimization problems that are difficult to optimize. Two parallelization methods are compared: a global parallel model in which solution evaluations are performed in parallel, and a hybrid model that treats the cooperative populations in a distributed manner while performing each solution evaluation in parallel. The first model is a straightforward parallelization, while the second one capitalizes on the characteristics of the coevolutionary framework. To investigate the efficacy of the proposed models, experiments are conducted on constrained multiobjective optimization problems, including complex characteristics, while varying the number of parallel cores up to 64. The experiments compare the two proposed methods from the viewpoint of search performance and execution time. The experimental results reveal that the latter hybrid model shows better computational efficiency and scalability against an increasing number of cores without adversely affecting the search performance compared to the former straightforward parallelization.

关键词： multiobjective evolutionary algorithm Constrained optimization problem Parallelization Speedup

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multiobjective multiple features fusion: A case study in image segmentation

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SWARM AND evolutionary COMPUTATION 2021年 60卷

作者： Liu, Cong Bian, Tingting Zhou, Aimin Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn 516 Jungong Rd Shanghai 200093 Peoples R China East China Normal Univ Sch Comp Sci & Technol 3663 Zhongshan Rd Shanghai 200062 Peoples R China

Most of existing image segmentation algorithms are only based on the color feature. However, the spatial distribution of an image can not be well described by using the color feature alone. Thus, it is necessary to add additional features to design efficient segmentation algorithms. Although many researchers also try to use multiple features for image segmentation, it is extremely difficult to combine multiple features automatically. This paper proposes a multiojective multiple features fusion strategy for image segmentation. The basic idea is to convert the segmentation problem into a multiobjective optimization problem, in which each objective considers one feature. It contains three steps. First, the original image is split into a set of over-segmented regions by using Meanshift to preserve the spatial details and to simplify the segmentation problem. Second, both the color and texture features are extracted to describe the regions. And two similarity matrices are designed by computing the similarity between each pair of regions in two features respectively. Third, a multiobjective evolutionary clustering algorithm is applied to merge these over-segmented regions. In this stage, two objective functions are designed based on the color and texture features respectively. A region index encoding scheme is introduced to design the individual, which contains some cluster representative regions. Some evolutionary operators are proposed to generate the new population. In the final generation, the best solution is selected from nondominated solutions for subsequent segmentation. Experiment results show that the proposed method provides promising segmentation results in combining the color and texture features.

关键词： Image segmentation Multiple features fusion Color and texture features multiobjective evolutionary algorithm

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Solving the RNA inverse folding problem through target structure decomposition and multiobjective evolutionary Computation

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Rubio-Largo, Alvaro Lozano-Garcia, Nuria Granado-Criado, Jose M. Vega-Rodriguez, Miguel A. Univ Extremadura Escuela Politecn Dept Comp & Telematics Syst Engn Caceres 10003 Spain Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Caceres 10003 Spain

The RNA inverse folding problem involves discovering a nucleotide sequence that folds into a desired target structure. Although numerous computational methods have been proposed over the years to tackle the problem, none have successfully solved the complete Eterna100 set. The Eterna100 set is widely recognized as a benchmark in this field. Therefore, there is still ample room for improvement in this area. This paper aims to address this challenge by introducing eM2dRNAs, an enhanced version of our previous approach called m2dRNAs, which is a multiobjective metaheuristic to design RNA sequences. By introducing eM2dRNAs, we aim to make significant advancements in RNA inverse folding. Our approach starts with the recursive decomposition of the target structure, simplifying the problem to be solved. We conducted a comparative study of our method against several published methods using the Eterna100 benchmark. The results showed that our proposal performs significantly better than the other methods across almost all metrics and categories considered, thus achieving our objective of improving the ability to solve the RNA inverse folding problem. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Bioinformatics RNA inverse folding multiobjective evolutionary algorithm Genetic algorithm Recursive decomposition

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