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Stable Specification Search in Structural Equation Models with Latent Variables

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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MRI appointment scheduling with uncertain examination time

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2019年第1期37卷 62-82页

作者： Qiu, Huaxin Wang, Dujuan Wang, Yanzhang Yin, Yunqiang Dalian Univ Technol Sch Management Sci & Engn Dalian 116023 Peoples R China KunmingUnivers Sci & Technol Data Sci Res Ctr Kunming 650093 Yunnan Peoples R China

This paper addresses the appointment scheduling problem for a single diagnostic facilitythe magnetic resonance imaging equipment, which provides several services to the appointed patients. The examinations have random service durations given by a joint discrete probability distribution. We consider two performance criteria: (1) the expected cost incurred from the equipment idle time and the examination overtime representing the operating costs of the hospital;and (2) the expected cost incurred from the patient waiting time reflecting the customer satisfaction. The overall goal is to identify the examination sequence and the scheduled start times for the appointed patients so as to minimize simultaneously the aforementioned criteria by determining all the Pareto-optimal schedules. The problem is first formulated as a two-stage stochastic integer programming model and it is shown to be NP-hard in the strong sense even for the case with only two scenarios. An improved multi-objective evolutionary algorithm is then proposed in the MOEA/D framework, where the uncertainty is simulated by constructing a number of different scenarios. To replace the time-consuming simulations during the process of evaluating the rescheduling cost, we integrate the algorithm with a support vector regression surrogate model which efficiently improves the robustness of the baseline schedule and the quality of the solution. Finally, using the real medical data, we assess the feasibility and effectiveness of the proposed model by comparing with the classical NSGA-II and the MOEA/D algorithm, and extract some appropriate management inspirations to medical staffs for decision-making references.

关键词： Appointment scheduling Duration uncertainty multi-objective evolutionary algorithm Support vector regression

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A DEA-based MOEA/D algorithm for portfolio optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 14477-14486页

作者： Zhou, Zhongbao Liu, Xianghui Xiao, Helu Wu, Shijian Liu, Yueyue Hunan Univ Sch Business Adm Changsha 410082 Hunan Peoples R China Hunan Normal Univ Sch Business Changsha 410081 Hunan Peoples R China Shandong Univ Sci & Technol Coll Econ & Management Qingdao 266590 Shandong Peoples R China Quzhou Univ Coll Entrepreneurship & Innovat Quzhou 324000 Peoples R China

In this paper, we present a multi-objective genetic algorithm DEA-MOEA/D by integrating decomposition method and DEA (Data Envelopment Analysis) approach. The initial solutions are generated by the DEA approach. Difference operators are adopted as the crossover operator of the parent. We adopt the test functions and portfolio optimization problems to compare the performance of DEA-MOEA/D, FDH-MOGA, MOEA/D and NSGA II. The results show that DEA-MOEA/D performs better than other three algorithms, not only for test functions, but for the portfolio optimization.

关键词： Portfolio optimization Data envelopment analysis multi-objective evolutionary algorithm Cardinality constraints

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An effective method for solving multiple travelling salesman problem based on NSGA-II

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2019年第2期7卷 121-129页

作者： Yang, Shuai Shao Yunfeng Zhang Kai Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Hubei Peoples R China

In this paper, an effective multi-objective evolutionary algorithm is proposed to solve the multiple travelling salesman problem. In order to obtain minimum total visited distance and minimum range between all salesmen, some novel representation, crossover and mutation operators are designed to enhance the local and global search behaviours, then NSGA-II framework is applied to find well-convergent and well-diversity non-dominated solutions. The proposed algorithm is compared with several state-of-the-art approaches, and the comparison results show the proposed algorithm is effective and efficient to solve the multiple travelling salesman problems.

关键词： multi-objective evolutionary algorithm multiple travelling salesman problem NSGA-II

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multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

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SN APPLIED SCIENCES 2019年第9期1卷 1072页

作者： Tey, Jing Yuen Shak, Katrina Pui Yee Univ Tunku Abdul Rahman Jalan Sungai Long Kajang 43000 Selangor Malaysia

Formula 1 is considered an important motorsport event that has driven the development of cutting-edge automotive technology. Similarly, virtual formula competition is introduced at the university level to harness technological advances for automotive development among universities. Performance and fuel efficiency have always been two conflicting objectives in determining the optimal powertrain design. In this paper, state-of-the-art multi-objective evolutionary algorithms (MOEAs) are employed to optimize the vehicle powertrain design based on competing event and the on-track performance through software in-the-loop optimization approach. Unlike conventional powertrain optimization approach where the powertrain design is optimized again standard urban driving profiles to achieve optimum driving performance and fuel consumption which is not suitable to be employed to optimize race car powertrain design. The powertrain design is optimized and benchmarked in multiple dynamic test events i.e. Acceleration, Autocross and Endurance. Each event measures different performance parameters (i.e. lap time and fuel consumption) A total of nine design parameters are optimized in this study. Three design parameters (employed to formulate the progressive gear calculation of six gear ratios), five gear shifting rpm (revolutions per minute) and an aerodynamic downforce scale value (for front and rear downforce). Two stages of optimization solution are proposed to handle such complicated design requirement. NSGA-II was identified as the most efficient algorithms among the MOEAs employed in this study;especially in terms of computational time, Pareto distribution and Pareto design. Overall, the optimized powertrain design showed significant improvement in all events i.e. energy efficiency was increased by 13.7% for the endurance event while lap time was reduced by 0.7 and 1.2% in the acceleration and autocross events, respectively.

关键词： Powertrain design multi-objective evolutionary algorithm Vehicle performance Fuel efficiency

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Seismic response control of adjacent high-rise buildings linked by the Tuned Liquid Column Damper-Inerter (TLCDI)

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ENGINEERING STRUCTURES 2020年 223卷 111169-111169页

作者： Wang, Qinhua Qiao, Haoshuai De Domenico, Dario Zhu, Zhiwen Tang, Yi Shantou Univ Dept Civil & Environm Engn Shantou 515063 Peoples R China Key Lab Struct & Wind Tunnel Guangdong Higher Edu Shantou 515063 Peoples R China Univ Messina Dept Engn I-98166 Messina Italy Shantou Univ Guangdong Engn Ctr Struct Safety & Hlth Monitorin Shantou 515063 Peoples R China China Acad Bldg Res Beijing 100013 Peoples R China

A novel passive control device termed Tuned Liquid Column Damper-Inerter (TLCDI) is adopted to control the seismic response of adjacent high-rise buildings. The inerter of the proposed TLCDI scheme exploits the large relative acceleration between the two adjacent buildings, producing a significant resisting force. Firstly, the governing nonlinear equations of motion of the TLCDI-linked adjacent high-rise buildings under earthquake excitation are formulated. For design purposes, the inherent nonlinear behavior of the TLCDI is linearized by adopting an equivalent stochastic linearization method. Subsequently, a benchmark structure consisting of two adjacent high-rise buildings linked by a corridor with a roller support and equipped with a TLCDI is examined. Eight characteristic parameters of the TLCDI are optimized through two distinct constrained multi-objective optimization problems by an evolutionary algorithm aimed at minimizing either the peak acceleration or the interstory-drift ratio of the two adjacent benchmark structures, respectively. Mitigation effects of the optimally designed TLCDIs on the responses of the benchmark structure are assessed considering a set of ten natural ground-motion records. It is found that the optimum TLCDI achieves mean reduction ratios of 15.7 and 26.1% on the peak acceleration of both the buildings, respectively. Conversely, the TLCDI fails to significantly reduce the interstory drift ratios. Robustness analysis of the optimally designed TLCDI is investigated under perturbations to the frequency and damping ratios. Some practical considerations are finally discussed concerning the feasibility of employing the proposed TLCDI in the considered benchmark project.

关键词： Tuned liquid column damper Inerter Adjacent high-rise buildings Optimal design multi-objective evolutionary algorithm Robustness analysis

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multi-objective Volleyball Premier League algorithm

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KNOWLEDGE-BASED SYSTEMS 2020年 196卷 105781-105781页

作者： Moghdani, Reza Salimifard, Khodakaram Demir, Emrah Benyettou, Abdelkader Persian Gulf Univ Computat Intelligence & Intelligent Optimizat Res Bushehr Iran Cardiff Univ Cardiff Business Sch Cardiff Wales USTOran Dept Informat BP 1505 El Mnaouer Oran Algeria

This paper proposes a novel optimization algorithm called the multi-objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely multi-objective Particle Swarm Optimization (MOPSO) and multi-objective evolutionary algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Global optimization Pareto solution Engineering design optimization problems

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multi-objective Sparse Reconstruction With Transfer Learning and Localized Regularization

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IEEE ACCESS 2020年 8卷 184920-184933页

作者： Yan, Bai Zhao, Qi Zhang, J. Andrew Wang, Zhihai Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Technol Sydney Global Big Data Technol Ctr GBDTC Ultimo NSW 2007 Australia Beijing Univ Technol Minist Educ Key Lab Optoelect Technol Beijing 100124 Peoples R China

multi-objective sparse reconstruction methods have shown strong potential in sparse reconstruction. However, most methods are computationally expensive due to the requirement of excessive functional evaluations. Most of these methods adopt arbitrary regularization values for iterative thresholding-based local search, which hardly produces high-precision solutions stably. In this article, we propose a multi-objective sparse reconstruction scheme with novel techniques of transfer learning and localized regularization. Firstly, we design a knowledge transfer operator to reuse the search experience from previously solved homogeneous or heterogeneous sparse reconstruction problems, which can significantly accelerate the convergence and improve the reconstruction quality. Secondly, we develop a localized regularization strategy for iterative thresholding-based local search, which uses systematically designed independent regularization values according to decomposed subproblems. The strategy can lead to improved reconstruction accuracy. Therefore, our proposed scheme is more computationally efficient and accurate, compared to existing multi-objective sparse reconstruction methods. This is validated by extensive experiments on simulated signals and benchmark problems.

关键词： Search problems Image reconstruction Knowledge transfer Iterative methods Convergence evolutionary computation Optimization Sparse reconstruction multi-objective evolutionary algorithm transfer learning regularization

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Combining mutual information and stable matching strategy for dynamic evolutionary multi-objective optimization

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ENGINEERING OPTIMIZATION 2018年第9期50卷 1434-1452页

作者： Fu, Xiaogang Sun, Jianyong Shanghai Dianji Univ Sch Elect Engn Shanghai Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China

It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence structure to facilitate population re-initialization in evolutionary search paradigms. The aim of the learning techniques is to discover knowledge from history information, thereby to track the movement of the optimal front quickly through good initialization when a change occurs. In this article, a new learning strategy is proposed, where the main ideas are (1) to use mutual information to identify the relationship between previously found approximated solutions;(2) to use a stable matching mechanism strategy to associate previously found optimal solutions bijectively;and (3) to re-initialize the new population based on a kinematics model. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.

关键词： multi-objective evolutionary algorithm dynamic multiobjective optimization kinematics model mutual information stable matching strategy

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AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction

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KNOWLEDGE-BASED SYSTEMS 2018年 146卷 58-72页

作者： Song, Shuangbao Gao, Shangce Chen, Xingqian Jia, Dongbao Qian, Xiaoxiao Todo, Yuki Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Fac Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

Despite half-century's unremitting efforts, the prediction of protein structure from its amino acid sequence remains a grand challenge in computational biology and bioinformatics. Two key factors are crucial to solving the protein structure prediction (PSP) problem: an effective energy function and an efficient conformation search strategy. In this study, we model the PSP as a multi-objective optimization problem. A three-objective evolution algorithm called AIMOES is proposed. AIMOES adopts three physical energy terms: bond energy, non-bond energy, and solvent accessible surface area. In AIMOES, an evolution scheme which flexibly reuse past search experiences is incorporated to enhance the efficiency of conformation search. A decision maker based on the hierarchical clustering is carried out to select representative solutions. A set of benchmark proteins with 30-91 residues is tested to verify the performance of the proposed method. Experimental results show the effectiveness of AIMOES in terms of the root mean square deviation (RMSD) metric, the distribution diversity of the obtained Pareto front and the success rate of mutation operators. The superiority of AIMOES is demonstrated by the performance comparison with other five state-of-the-art PSP methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Reuse search experience Hierarchical clustering Protein structure prediction

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