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Single-Cell RNA Sequencing Data Interpretation by Evolutionary multiobjective Clustering

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第5期17卷 1773-1784页

作者： Li, Xiangtao Wong, Ka-Chun Jilin Univ Sch Artificial Intelligence Changchun 130117 Jilin Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

In recent years, single-cell RNA sequencing reveals diverse cell genetics at unprecedented resolutions. Such technological advances enable researchers to uncover the functionally distinct cell subtypes such as hematopoietic stem cell subpopulation identification. However, most of the related algorithms have been hindered by the high-dimensionality and sparse nature of single-cell RNA sequencing (RNA-seq) data. To address those problems, we propose a multiobjective evolutionary clustering based on adaptive non-negative matrix factorization (MCANMF) for multiobjective single-cell RNA-seq data clustering. First, adaptive non-negative matrix factorization is proposed to decompose data for feature extraction. After that, a multiobjective clustering algorithm based on learning vector quantization is proposed to analyze single-cell RNA-seq data. To validate the effectiveness of MCANMF, we benchmark MCANMF against 15 state-of-the-art methods including seven feature extraction methods, seven clustering methods, and the kernel-based similarity learning method on six published single-cell RNA sequencing datasets comprehensively. When compared with those 15 state-of-the-art methods, MCANMF performs better than the others on those single-cell RNA sequencing datasets according to multiple evaluation metrics. Moreover, the MCANMF component analysis, time complexity analysis, and parameter analysis are conducted to demonstrate various properties of our proposed algorithm.

关键词： Clustering algorithms Sequential analysis RNA Linear programming Matrix decomposition Feature extraction Sociology Single-cell RNA sequencing multiobjective algorithm bioinformatics computational biology

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Optimal Placement of New Isolation Valves in a Water Distribution Network Considering Existing Valves

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2022年第6期148卷 04022032-04022032页

作者： Yang, Zhijiang Guo, Song Hu, Zhen Yao, Dan Wang, Lihui Yang, Binbin Liang, Xun China Univ Geosci Sch Geog & Informat Engn Wuhan 430074 Hubei Peoples R China Wuhan Univ Wuhan HopeTop Technol Co Ltd Sci PkWuhan East Lake High Tech Dev Zone Wuhan 430040 Hubei Peoples R China Glodon Co Ltd 166 Mingguang Rd Xian 710000 Shanxi Peoples R China

There are often planned (for example, regular maintenance) and unplanned (for example, pipe bursts) interruptions in a water distribution network (WDN). Therefore, part of the isolation valves must be closed to isolate the part (segment) of the network that contains one or more pipes. Isolation of the target pipe segment with minimum possible disruption has been a problem to be solved. A large number of studies have been conducted to optimize the design of isolation valve placement in new WDNs, but less attention has been given to reducing the isolation zone and improving the reliability of old WDNs by adding optimally placed isolation valves. Therefore, this paper proposes a multiobjective optimization model for adding optimally located isolation valves to old WDNs, which considers the dual objectives of economy and reliability. The installation or removal of isolation valves can cause the original segments to split or merge, so this paper proposes the use of the segment-valve (SV) graph local update (SVLU) algorithm instead of the seed-filling algorithm to construct the SV graph. The optimization model was applied to the valve layout modification of part of the WDN in Changshu, Jiangsu Province, China, and the results showed that the model can solve the optimization solution quickly (15.358 s). Moreover, the use of the SVLU algorithm improved the efficiency of the solved model by 26.71%.

关键词： Segment Isolation valves Weight average demand shortfall Water distribution network (WDN) multiobjective algorithm

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multiobjective Program and Hybrid Imperialist Competitive algorithm for the Mixed-Model Two-Sided Assembly Lines Subject to Multiple Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第1期48卷 119-129页

作者： Li, Dashuang Zhang, Chaoyong Tian, Guangdong Shao, Xinyu Li, Zhiwu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Jilin Univ Transportat Coll Changchun 130022 Jilin Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Shaanxi Peoples R China

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.

关键词： two-sided assembly line multiobjective algorithm late acceptance hill-climbing (LAHC) Imperialist competitive algorithm (ICA)

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A multiobjective Simulated Kalman Filter Optimization algorithm 4

A Multiobjective Simulated Kalman Filter Optimization Algori...

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4th IEEE International Conference on Applied System Invention (IEEE ICASI)

作者： Azwan, A. Razak, A. Jusof, M. F. M. Nasir, A. N. K. Ahmad, M. A. UMP Fac Elect & Elect Engn FKEE Pekan Pahang 26600 Malaysia

ISBN: (纸本)9781538643426

This paper presents a new multiobjective type optimization algorithm known as a multiobjective Optimization Simulated Kalman Filter (MOSKF). It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. A synergy between SKF and Non-dominated Solution (NS) approach is introduced to formulate the multiobjective type algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. A Non-dominated Solution (NS) approach is utilized in the formulation of the multiobjective strategy. Cost function value and diversity spacing parameters are taken into consideration in the strategy. Every single agent carries those two parameters in which will be used to compare with other solutions from other agents in order to determine its domination. A solution that has a lower cost function value and higher diversity spacing is considered as a solution that dominates other solutions and thus is ranked in a higher ranking. The algorithm is tested with various multiobjective benchmark functions and compared with Non-Dominated Sorting Genetic algorithm 2 (NSGA2) multiobjective algorithm. Result of the analysis on the accuracy tested on the benchmark functions is tabulated in a table form and shows that the proposed algorithm outperforms NSGA2 significantly. The result also is presented in a graphical form to compare the generated Pareto solution based on proposed MOSKF and original NSGA2 with the theoretical Pareto solution.

关键词： Simulated Kalman Filter Non-dominated Sorting multiobjective algorithm

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Multi-objective colliding bodies optimization algorithm for design of trusses

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2019年第1期6卷 49-59页

作者： Kaveh, Ali Mahdavi, Vahid Reza Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn POB 16846-13114 Tehran Iran Iran Univ Sci & Technol Dept Civil Engn POB 16846-13114 Tehran Iran

This article presents a new population-based optimization algorithm to solve the multi-objective optimization problems of truss structures. This method is based on the recently developed single-solution algorithm proposed by the present authors, so called colliding bodies optimization (CBO), with each agent solution being considered as an object or body with mass. In the proposed multi-objective colliding bodies optimization (MOCBO) algorithm, the collision theory strategy as the search process is utilized and the Maximin fitness procedure is incorporated to the CBO for sorting the agents. A series of well-known test functions with different characteristics and number of objective functions are studied. In order to measure the accuracy and efficiency of the proposed algorithm, its results are compared to those of the previous methods available in the literature, such as SPEA2, NSGA-II and MOPSO algorithms. Thereafter, two truss structural examples considering bi-objective functions are optimized. The performance of the proposed algorithm is more accurate and requires a lower computational cost than the other considered algorithms. In addition, the present methodology uses simple formulation and does not require internal parameter tuning. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.

关键词： Design optimization multiobjective algorithm Truss structural optimization Colliding bodies optimization algorithm Maximin method

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multiobjective Patient Stratification Using Evolutionary multiobjective Optimization

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2018年第5期22卷 1619-1629页

作者： Li, Xiangtao Wong, Ka-Chun Northeast Normal Univ Dept Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

One of the main challenges in modern medicine is to stratify patients for personalized care. Many different clustering methods have been proposed to solve the problem in both quantitative and biologically meaningful manners. However, existing clustering algorithms suffer from numerous restrictions such as experimental noises, high dimensionality, and poor interpretability. To overcome those limitations altogether, we propose and formulate a multiobjective framework based on evolutionary multiobjective optimization to balance the feature relevance and redundancy for patient stratification. To demonstrate the effectiveness of our proposed algorithms, we benchmark our algorithms across 55 synthetic datasets based on a real human transcription regulation network model, 35 real cancer gene expression datasets, and two case studies. Experimental results suggest that the proposed algorithms perform better than the recent state-of-the-arts. In addition, time complexity analysis, convergence analysis, and parameter analysis are conducted to demonstrate the robustness of the proposed methods from different perspectives. Finally, the t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to project the selected feature subsets onto two or three dimensions to visualize the high-dimensional patient stratification data.

关键词： Patient stratification multiobjective algorithm clustering

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A Composite Entropy Model in a multiobjective Framework for Gene Regulatory Networks

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CURRENT BIOINFORMATICS 2018年第1期13卷 85-94页

作者： Majumder, Aurpan Sarkar, Mrityunjay Dash, Harihar Akhilesh, Indupalli NIT Durgapur Fac Engn Dept ECE Durgapur 713209 India

Background: Transcription Factors (TFs) play a pivotal role in a Gene Regulatory Network (GRN) by differentially regulating genes across conditions. In some cases, it requires coordinated regulation of multiple TFs to control a Differentially Expressed (DE) gene. In this line, we have also developed simple architectures to unveil the parallel regulatory control by TFs. Objective: To date there are few works that have conducted active research to develop serial TF regulatory paths. In order to make some contribution in this specific area, here we have proposed an algorithm which puts up an architecture of multiple serial TF regulatory paths for a target gene. Methods: In order to explore the full potential of our algorithm we have tested it on one synthetic and three eukaryotic organism gene expression datasets. We were able to construct multiple transcription factor regulatory paths with varying lengths to each target differentially expressed gene with such transcription factors distributed across various multiobjective optimal fronts based on their regulatory properties. This is followed by multiple stage minimal entropy analysis. Conclusion: Through this multiple stage composite entropy approach we have not only assessed the strength of transcription factor to target interaction pathways supported by different literatures but added some new interactions and deleted a few existing ones having weak regulatory control probabilities.

关键词： Regulatory Impact Factor (RIF) Differential Correlation Set Analysis (dCSA) Partial Correlation with Information Theory (PCIT) multiobjective algorithm regulator target entropy steepest descent

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multiobjective Environment/Economic Power Dispatch Using Evolutionary multiobjective Optimization

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IEEE ACCESS 2018年 6卷 13066-13074页

作者： Ma, Shijing Wang, Yunhe Lv, Yinghua Northeast Normal Univ Dept Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China Northeast Normal Univ Coll Humanities & Sci Dept Comp Sci Changchun 130117 Jilin Peoples R China

Environmental/economic dispatch (EED) problems play a salient role in the power system, which can be defined as a complex constrained optimization problem. Many different methods have been introduced to handle EED problems and got some inspiring positive results in the research. In this paper, a new multiobjective global best artificial bee colony (ABC) algorithm is proposed to tackle multiobjective EED problems. To manipulate this problem effectively, we propose a global best ABC algorithm to generate the new individual to speed up the convergence of the proposed algorithm. Afterwards, a crowding distance assignment approach is employed to evolve the population. Finally, a straightforward constraint checking procedure is used to tackle those different constraints of EED problems. Experimental results can conclude that MOGABC can provide best solutions in solving multiobjective EED problems.

关键词： Environmental/economic dispatch multiobjective algorithm artificial bee colony

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multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2010年第12期22卷 1738-1751页

作者： Chen, Huanhuan Yao, Xin Univ Birmingham CERCIA Sch Comp Sci Birmingham B15 2TT W Midlands England

Negative Correlation Learning (NCL) [1], [2] is a neural network ensemble learning algorithm which introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean-square-error (MSE) together with the correlation. This paper describes NCL in detail and observes that the NCL corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This insight explains that NCL is prone to overfitting the noise in the training set. The paper analyzes this problem and proposes the multiobjective regularized negative correlation learning (MRNCL) algorithm which incorporates an additional regularization term for the ensemble and uses the evolutionary multiobjective algorithm to design ensembles. In MRNCL, we define the crossover and mutation operators and adopt nondominated sorting algorithm with fitness sharing and rank-based fitness assignment. The experiments on synthetic data as well as real-world data sets demonstrate that MRNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set. In the experimental discussion, we give three reasons why our algorithm outperforms others.

关键词： multiobjective algorithm multiobjective learning neural network ensembles neural networks negative correlation learning regularization

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Multi-Objective Tuning of Nonlinear PID Controllers for Greenhouse Environment using Evolutionary algorithms

Multi-Objective Tuning of Nonlinear PID Controllers for Gree...

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2010 IEEE World Congress on Computational Intelligence

作者： Hu, Haigen Xu, Lihong Wei, Ruihua Zhu, Bingkun Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China Tongji Univ Dept Traff & Transportat Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781424481262

This paper investigates the issue of PID-controller parameters tuning for a greenhouse climate control system using Evolutionary algorithms based on multiple performance measures such as good set-point tracking and smooth control signals. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is validated for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a series of simulations. The results show that the controllers by tuning the gain parameters can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Maybe it is quite an effective and promising tuning method using multi-objective algorithms in the complex greenhouse production.

关键词： control increment evolutionary algorithms evolutionary computation greenhouse climate control system greenhouse environment greenhouses integrated time square error multiobjective algorithm multiobjective optimization problem multiobjective tuning multiple performance measure nonlinear PID controller nonlinear control systems nonlinear thermodynamic law optimisation set-point tracking smooth control signals three-term control

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