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multi-objective-Based Radiomic Feature Selection for Lesion Malignancy Classification

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第1期24卷 194-204页

作者： Zhou, Zhiguo Li, Shulong Qin, Genggeng Folkert, Michael Jiang, Steve Wang, Jing UT Southwestern Med Ctr Med Artificial Intelligence & Automat Lab Dallas TX 75390 USA UT Southwestern Med Ctr Dept Radiat Oncol Dallas TX 75390 USA Southern Med Univ Sch Biomed Engn Guangzhou 510515 Peoples R China

objective: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. Methods: this work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy-based termination criterion that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. Results: we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. Conclusion: the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. Significance: the proposed method is general and more effective radiomic feature selection strategy.

关键词： Feature extraction Lesions Entropy Informatics Sociology Statistics Linear programming Radiomics lesion malignancy classification feature selection multi-objective evolutionary algorithm evidential reasoning

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Methods That Optimize multi-objective Problems: A Survey and Experimental Evaluation

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IEEE ACCESS 2020年 8卷 80855-80878页

作者： Taha, Kamal Khalifa Univ Dept Elect & Comp Sci Abu Dhabi U Arab Emirates

Most current multi-optimization survey papers classify methods into broad objective categories and do not draw clear boundaries between the specific techniques employed by these methods. This may lead to the misclassification of unrelated methods/techniques into the same objective category. Moreover, most of these survey papers classify algorithms as independent of the specific techniques they employ. Toward this end, we introduce in this survey paper a methodology-based taxonomy that classifies multi-optimization methods into hierarchically nested, fine-grained, and specific classes. We provide a methodological taxonomy to classify methods into the following hierarchical fashion: objective categories objective functionsoptimization methodsoptimization sub-methods. We introduce a comprehensive survey on the methods that are contained under each optimization method, the optimization methods contained under each objective function, and objective functions contained under each objective category. We selected the objective functions that should be maximized for solving most real-word multi-objective optimization problems, which are pairs of the following: partitions separability, internal density, dynamic similarity, and structural similarity. For each optimization method, we surveyed the various algorithms in literature that pertain to the method. We experimentally compared and ranked the optimization methods that fall under each objective function, the objective functions that fall under each objective category, and the objective categories used for solving a specific optimization problem.

关键词： Linear programming Optimization methods Classification algorithms Sociology Statistics Taxonomy multi-objective optimization multi-objective problem multi-objective evolutionary algorithm objective function

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On the constraint of passive RFID sensor tag design with MOEA/D-DE

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2017年第1期59卷 83-86页

作者： Song, Xiaotian Wang, Gang Yang, Chenwei He, Yuxing Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

For passive RFID sensor tag, high sensing sensitivity is achieved at the expense of read range reduction with a fixed transmitted power of RFID reader. To maximize sensing sensitivity and ensure satisfied communication performance of tag at meanwhile, the constraint between them can be defined as a multi-objective problem. The MOEA/D-DE (multi-objective evolutionary algorithm based on decomposition combined with differential evolution) is applied for the first time to provide a direct and effective solution to this constraint. For demonstration, an effective permittivity sensor and a temperature sensor are designed with MOEA/D-DE, respectively. Both the simulated and measured results show the feasibility of applying MOEA/D-DE to design high-sensitivity sensor tag with satisfied communication performance. (c) 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:83-86, 2017

关键词： RFID sensor sensing sensitivity read range multi-objective evolutionary algorithm

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Decision variable classification based multi-objective multifactorial memetic algorithm for multi-objective multi-task optimization problem

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Xu, Zhiwei Xu, Jiafeng Zhang, Kai Xu, Xin He, Juanjuan Wu, Ni Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Rea Wuhan 430065 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

multi-task multi-objective optimization problems need to consider the algorithm's convergence and the population's diversity. The information transfer of decision variables with different characteristics may harm the effect of knowledge reuse. This paper proposes a novel hybrid multi-objective multifactorial memetic algorithm to address this issue. The proposed variable classification method will classify decision variables into convergence-related and diversity-related decision variables. Only the same type of decision variables in the source and target tasks can transfer information to avoid negative transfer. Different evolutionary operators are adopted according to the characteristics of decision variables during individual recombination. In addition, the proposed algorithm hybridizes the immune algorithm as the global evolutionary operator and the evolutionary gradient search algorithm as the local search operator into the multifactorial framework to enhance the searching ability. Finally, the proposed algorithm is compared with the state-of-the-art multi-objective evolutionary multitasking algorithms. The results of the experiments show that the proposed algorithm can achieve promising performance on the classical and complex multi-task multi-objective benchmark test suites.

关键词： evolutionary multitasking multi-objective evolutionary algorithm evolutionary gradient search Decision variable classification multi-objective multi-task optimization problem

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Optimizing Model Base Predictive Control for Combustion Boiler Process at High Model Uncertainty

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CHEMICAL AND BIOCHEMICAL ENGINEERING QUARTERLY 2017年第3期31卷 313-324页

作者： Osman, H. M. King Khalid Univ Dept Chem Engn PO 394 Abha Saudi Arabia

This paper proposes a multi-objective evolutionary algorithm for optimizing model base predictive control (MBPC) tuning parameters applied to the boiling process. The multi-objective evolutionary algorithms are able to incorporate many objective functions that can simultaneously meet robust stability and performance that can satisfy control design objective functions. These promising techniques are successfully implemented to stabilise MBPC at the implications of different levels of model uncertainties. The Pareto optimum technique is able to overcome the problem of trapping the standard genetic algorithms (SGAs) in the local optimum when using the LQ as the objective functions at the price of high model uncertainty. Introducing robust stability and performance objective functions has successfully improved the search procedure for MBPC tuning variables at high model uncertainty.

关键词： model base predictive control multi-objective evolutionary algorithm control tuning

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Optimization of RNN-LSTM Model Using NSGA-II algorithm for IOT-based Fire Detection Framework

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IETE JOURNAL OF RESEARCH 2024年第7期70卷 6239-6254页

作者： Roy, Tanushree Shome, Saikat kumar Govt India Cent Mech Engn Res Inst Minist Sci & Technol CSIR Durgapur 713209 India

The occurrence of fire leads to unparalleled loss of resources as well as human life and hence, fire detection systems must be trustworthy and less erroneous. Real-time assessment of fire conditions through predictive learning models could lead to easier decision-making and timely rescue operations. Reported works are often restricted to the use of singular rule-based algorithms which can hardly offer a comprehensive solution by adapting to changing dynamics of fire conditions due to their static features, mostly leading to inaccurate classification. In this research, an efficient fire prediction framework has been proposed by efficiently combining the outputs of recurrent neural networks which bear the advantages of short-term predictions with long short-term memory that takes care of long-term predictions. Weights to combine multi-objective optimization functions to minimize mean absolute error and root mean square error have been designed using non-dominated sorting genetic algorithm II (NSGA-II) which offers a lower time complexity. The benchmark dataset from the NIST website has been chosen for analysis and performance validation of the proposed classifier on experimental data pertaining to different fire scenarios. A Pareto optimal front has been obtained from the proposed algorithm which represents the optimum solutions. The performance of the proposed model has been exhaustively evaluated through different factors such as accuracy, RMSE, MAE, F-Measure, binary classification rate, negative predictive value, recall and precision which justifies its contribution. The proposed model reduced RMSE by 14.95-19.88% compared to baseline machine learning models along with an enhanced accuracy of 95.05% and reduced false positive rate which is better compared to reported works along with improvement in F-Measure. Results show that the proposed NSGA-II-based RNN LSTM model accurately predicts the occurrence of fire events with reduced false alarms while maintaining a l

关键词： Fire alarm Long short-term memory multi-objective evolutionary algorithm Nondominated sorting genetic algorithm Recurrent neural network Time series

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Minable Data Publication Based on Sensitive Association Rule Hiding

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第5期6卷 1247-1257页

作者： Yang, Fan Lei, Xinyu Le, Junqing Mu, Nankun Liao, Xiaofeng Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Minable data publication can promote data sharing among commercial companies and further facilitate the development of data-driven services. However, these commercial companies are often reluctant to publish their data due to security concerns. The published data may contain some sensitive information that is minable by malicious entities, leading to data privacy leakage. Therefore, it is highly demanded to develop the technologies supporting minable data publication with privacy protection. In this paper, we propose a privacy-preserved minable data publication scheme (PMDP ). PMDP enables selective sensitive association rules hiding while supporting the association rule mining. In PMDP, how to balance the trade-off between data privacy and data utility is the major problem, which can be formulated as a multi-objective optimization problem. To tackle this multi-objective optimization problem, we develop a customized multi-objective evolutionary algorithm (MOEA). In the customized MOEA, the local optimum trapping issue and slow convergence speed issue are hard to be addressed. First, to avoid being trapped into the local optimum, we carefully design a novel mutation method to guarantee the diversity of solutions. Second, to accelerate the convergence speed, we present a preprocessing method before the evolution process of the MOEA. In addition, we introduce the elite learning strategy into the MOEA, so the convergence speed can be further accelerated. At last, experiments are conducted over several datasets to demonstrate the effectiveness of PMDP.

关键词： Convergence Data privacy Optimization Computational intelligence Statistics Sociology Privacy Minable data publication association rule hiding multi-objective evolutionary algorithm

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Reinforcement learning for decision-making under deep uncertainty

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 359卷 120968页

作者： Pei, Zhihao Rojas-Arevalo, Angela M. de Haan, Fjalar J. Lipovetzky, Nir Moallemi, Enayat A. Univ Melbourne Fac Engn & Informat Technol Sch Comp & Informat Syst Melbourne Australia Univ Melbourne Melbourne Ctr Data Sci Melbourne Australia Commonwealth Sci & Ind Res Org CSIRO Melbourne Australia 700 Swanston St Carlton Vic 3053 Australia

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decisionmaking efficiency. This paper introduces Reinforcement Learning (RL) that employs closed -loop control as a new exploration method that enables automated adaptive policy -making for planning under uncertainty. To investigate its performance, we compare RL with a widely -used exploration method, multi -objective evolutionary algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications.

关键词： Deep uncertainty Exploratory modeling Reinforcement learning multi-objective evolutionary algorithm Adaptation Robustness

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Stable Specification Search in Structural Equation Models with Latent Variables

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2019年第5期10卷 1-23页

作者： Rahmadi, Ridho Groot, Perry Heskes, Tom Univ Islam Indonesia Informat Jalan Kaliurang Km 14 Yogyakarta 55284 Indonesia Radboud Univ Nijmegen Inst Comp & Informat Sci Data Sci Nijmegen Netherlands

In our previous study, we introduced stable specification search for cross-sectional data (S3C). It is an exploratory causal method that combines the concept of stability selection and multi-objective optimization to search for stable and parsimonious causal structures across the entire range of model complexities. S3C, however, is designed to model causal relations among observed variables. In this study, we extended S3C to S3C-Latent, to model linear causal relations between latent variables that are measured through observed proxies. We evaluated S3C-Latent on simulated data and compared the results to those of PC-MIMBuild, an extension of the PC algorithm, the state-of-the-art causal discovery method. The comparison shows that S3C-Latent achieved better performance. We also applied S3C-Latent to real-world data of children with attention deficit/hyperactivity disorder and data about measuring mental abilities among pupils. The results are consistent with those of previous studies.

关键词： Causal modeling structural equation model with latent variables specification search stability selection multi-objective evolutionary algorithm

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multi-objective game theory optimization for balancing economic, social and ecological benefits in the Three Gorges Reservoir operation

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ENVIRONMENTAL RESEARCH LETTERS 2021年第8期16卷

作者： Yu, Yang Zhao, Rui Zhang, Jiahe Yang, Douqiang Zhou, Tianyu Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China Hohai Univ Dept Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Reservoir operation is an important and effective measure for realizing optimal allocation of water resources. It can effectively alleviate regional scarcity of water resources, flood disasters and other social problems, and plays an important role in supporting sustainable strategic development of water resources. Coordinating the stakeholders is key to the smooth operation of a multifunctional reservoir. This research examines the competition among stakeholders of a multi-objective ecological reservoir operation aiming to provide for economic, social and ecological demands. A multi-objective game theory model (MOGM) specified 10-day water discharge to meet the triple water demands (power generation, socio-economic consumption and environment) for multi-purpose reservoir operation. The optimal operation of the Three Gorges Reservoir (TGR), with the ecological objective of providing comprehensive ecological flow demanded for some key ecological problems that may occur in the middle and lower reaches of the Yangtze River, was chosen as a case study. Discharged water calculated by the MOGM and a conventional multi-objective evolutionary algorithm/decomposition with a differential evolution operator was then allocated to different demands. The results illustrate the applicability and efficiency of the MOGM in balancing transboundary water conflicts in multi-objective reservoir operation that can provide guidance for the operation of the TGR.

关键词： ecological reservoir operation game theory multi-objective evolutionary algorithm transboundary water conflicts water allocation

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