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Dynamic Resource Scheduling for C2 Organizations Based on multi-objective Optimization

IEEE ACCESS 2019年 7卷 64614-64626页

作者： Xun Wang Yao, Peiyang Zhang, Jieyong Wan Lujun Jiao, Zhiqiang Air Force Engn Univ Informat & Nav Coll Xian 710077 Shaanxi Peoples R China Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Shaanxi Peoples R China

The dynamic resource scheduling problem is a field of intense research in command and control organization mission planning. This paper analyzes the emergencies in the battlefield first and divides them into three categories: the changing of task attributes, reduction of available platforms, and change in the number of tasks. To deal with these emergencies, in this paper, we built a series of multi-objective optimization models that maximizes the task execution quality and minimizes the cost of plan adjustment. To solve the model, we proposed an improved multi-objective evolutionary algorithm. A type of mapping operator and an improved crowding-distance sorting method are designed for the algorithm. Finally, the availability of the model and the solving algorithm were proved through a series of experiments. The Pareto frontier for the multi-objective dynamic resource scheduling problem can be found effectively, and the algorithm proposed in this paper shows better convergence compared with the AMP-NSGA-II algorithm.

关键词： Command and control organization dynamic resource scheduling multi-objective evolutionary algorithm multi-objective optimization

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A comprehensive multi-objective approach of service selection for service processes with twofold restrictions

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 119-140页

作者： Liang, Helan Du, Yanhua Jiang, Ting Li, Fanzhang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 700083 Peoples R China

To build complex business processes with QoS constraints by services with inter-service correlations is a challenge. Meeting this challenge involves the QoS-aware service selection problem for service processes with twofold restrictions (QoS constraints and inter-service correlations). Recently, QoS-aware service selection has been addressed from a multi-objective perspective. However, existing research fails to comprehensively consider both QoS constraints and inter-service correlations that widely exist in a real business world. Besides, none of them comprehensively considers both service selection at build time and run time, which may lead to high economic compensation caused by breach penalties at run time. In this paper, we present a comprehensive multi-objective optimization model for service selection with twofold restrictions. Furthermore, an improved multi-objective evolutionary algorithm framework for this model (MOEAQI) is proposed, which can provide a systematic solution for service selection at build time and run time respectively. In this framework, a constrained-domination principle is designed for the specific objectives and constraints in different phases, and several improved strategies are proposed to increase the searching effectiveness and efficiency. Finally, three sets of experiments with different problem sizes are constructed, and our approach is tested in comparison to the state-of-the-art algorithms for service selection problems including GDE3, E(3)MOGA and DASC. The performance of each algorithm is evaluated in the aspects of set coverage, spacing and computation time. Experimental results demonstrate that our approach is more effective than existing methods for service selection with twofold restrictions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Service selection Service process QoS constraint Inter-service correlation multi-objective evolutionary algorithm

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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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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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