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25th ACM International Systems and Software Product Line Conference (SPLC)

作者： Ballesteros, Joaquin Fuentes, Lidia Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9781450384704

Dynamic Software Product Lines (DSPLs) are a well-accepted approach for self-adapting Cyber-Physical Systems (CPSs) at run-time. The DSPL approaches make decisions supported by performance models, which capture system features' contribution to one or more optimization goals. Combining performance models with Multi-Objectives Evolutionary algorithms (MOEAs) as decision-making mechanisms is common in DSPLs. However, MOEAs algorithms start solving the optimization problem from a randomly selected population, not finding good configurations fast enough after a context change, requiring too many resources so scarce in CPSs. Also, the DSPL engineer must deal with the hardware and software particularities of the target platform in each CPS deployment. And although each system instantiation has to solve a similar optimization problem of the DSPL, it does not take advantage of experiences gained in similar CPS. Transfer learning aims at improving the efficiency of systems by sharing the previously acquired knowledge and applying it to similar systems. In this work, we analyze the benefits of transfer learning in the context of DSPL and MOEAs testing on 8 feature models with synthetic performance models. Results are good enough, showing that transfer learning solutions dominate up to 71% of the non-transfer learning ones for similar DSPL.

关键词： Dynamic Software Product Lines Transfer Learning multiobjective optimization algorithms Self-adaptation Cyber-Physical Systems

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Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven multiobjective Evolutionary Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第8期52卷 7388-7401页

作者： Juang, Chia-Feng Chou, Ching-Yu Lin, Chin-Teng Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan Univ Technol Sydney Australian AI Inst CIBCI Lab FEIT Sydney NSW 2007 Australia

This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.

关键词： Robot sensing systems Navigation Mobile robots Task analysis optimization PD control Fuzzy control Data-driven fuzzy systems evolutionary fuzzy control multiobjective optimization algorithms obstacle avoidance robot navigation

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3D Terrain multiobjective Deployment optimization of Heterogeneous Directional Sensor Networks in Security Monitoring

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IEEE TRANSACTIONS ON BIG DATA 2019年第4期5卷 495-505页

作者： Cao, Bin Zhao, Jianwei Lv, Zhihan Liu, Xin Hebei Univ Technol Sch Comp Sci & Engn Tianjin 300401 Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510275 Guangdong Peoples R China Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China UCL Dept Comp Sci 66-72 Gower St London WC1E 6EA England Hebei Univ Technol Tianjin 300401 Peoples R China

The traditional deployment research on wireless sensor networks (WSNs) has mainly focused on 2D plane and 3D full space;also, the sensors considered are almost always omni-directional sensors and are usually homogeneous. However, this type of research cannot fulfill the diverse requirements required for practical 3D environment. We study the deployment problem of heterogeneous directional sensor networks (HDSNs) on 3D terrain, which is more suitable for practical security monitoring requirements and has more practical significance. In this paper, we propose a novel uncertain comprehensive coverage model: a modified 3D directional sensing model is presented, and a non-probabilistic measure based fusion operator is utilized. We transform the deployment problem into a multiobjective optimization problem by comprehensively considering Coverage, Connectivity Uniformity and Deployment Cost, and we use various state-of-the-art multiobjective optimization algorithm based deployment approaches to address it. We conduct deployment experiments on three types of real-world 3D terrain data (plain, hill and mountain). Through the analysis of the deployment results, we obtain a deeper understanding and insight into the multiobjective deployment problem of HDSN. Meanwhile, we more clearly recognize the characteristics of the deployment approaches.

关键词： Heterogeneous directional sensor networks (HDSNs) non-probabilistic measure multiobjective optimization algorithms 3D terrain

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multiobjective Optimal Control of Wind Turbines: A Survey on Methods and Recommendations for the Implementation

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ENERGIES 2022年第2期15卷 567-567页

作者： Gambier, Adrian IWES Fraunhofer Inst Wind Energy Syst Seedeich 45 D-27572 Bremerhaven Germany

Advanced control system design for large wind turbines is becoming increasingly complex, and high-level optimization techniques are receiving particular attention as an instrument to fulfil this significant degree of design requirements. multiobjective optimal (MOO) control, in particular, is today a popular methodology for achieving a control system that conciliates multiple design objectives that may typically be incompatible. multiobjective optimization was a matter of theoretical study for a long time, particularly in the areas of game theory and operations research. Nevertheless, the discipline experienced remarkable progress and multiple advances over the last two decades. Thus, many high-complexity optimization algorithms are currently accessible to address current control problems in systems engineering. On the other hand, utilizing such methods is not straightforward and requires a long period of trying and searching for, among other aspects, start parameters, adequate objective functions, and the best optimization algorithm for the problem. Hence, the primary intention of this work is to investigate old and new MOO methods from the application perspective for the purpose of control system design, offering practical experience, some open topics, and design hints. A very challenging problem in the system engineering application of power systems is to dominate the dynamic behavior of very large wind turbines. For this reason, it is used as a numeric case study to complete the presentation of the paper.

关键词： multiobjective optimization multiobjective optimal control controller tuning objective functions multiobjective optimization algorithms wind turbines control

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Reinforcement Neural Fuzzy Surrogate-Assisted multiobjective Evolutionary Fuzzy Systems With Robot Learning Control Application

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第3期28卷 434-446页

作者： Juang, Chia-Feng Trong Bac Bui Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan

This paper proposes a new reinforcement neural fuzzy surrogate (RNFS)-assisted multiobjective evolutionary optimization (RNFS-MEO) algorithm to boost the learning efficiency of data-driven fuzzy controllers (FCs). The RNFS-MEO is applied to evolve a population of FCs in a multiobjective robot wall-following control problem in order to reduce the number of time-consuming control trials and the implementation time of learning. In the RNFS-MEO, the RNFS is incorporated into a typical multiobjective continuous ant colony optimization algorithm to improve its learning efficiency. The RNFS estimates the accumulated multiobjective function values of the FCs in a colony without applying them to control a process, which helps reduce the number of control trials. The RNFS is trained online through structure and parameter learning based on the reinforcement signals from controlling a process. Considering the influence of the current control signals on the future states of a controlled process, the temporal difference technique is used in the RNFS training so that it estimates not only the current but also the future objective function values. The colony of FCs in the RNFS-MEO is repeatedly evolved based on the RNFS estimated values or the objective function values from real evaluations until a colony of successful FCs is found. The RNFS-MEO-based FC learning approach is applied to a robot wall-following control problem. Simulations and experiments on the robot control application are performed to verify the effectiveness and efficiency of the RNFS-MEO.

关键词： optimization Process control Fuzzy control Robots Training Mathematical model Evolutionary fuzzy systems (EFS) multiobjective optimization algorithms neural fuzzy systems surrogate-assisted learning temporal difference (TD) learning

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multiobjective Evolution of Biped Robot Gaits Using Advanced Continuous Ant-Colony Optimized Recurrent Neural Networks

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第6期48卷 1910-1922页

作者： Juang, Chia-Feng Yeh, Yen-Ting Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan Silicon Mot Technol Corp Design Team2 Hsinchu 302 Taiwan

This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimized to generate angles of the hip roll and pitch, the knee pitch, and the ankle pitch and roll. The performance of the FCRNN-generated gait is evaluated according to the walking speed, trajectory straightness, oscillations of the body in the pitch and yaw directions, and walking posture, subject to the basic constraints that the robot cannot fall down and must walk forward. This paper formulates this gait generation task as a constrained multiobjective optimization problem and solves this problem through an AMO-CACO-based evolutionary learning approach. The AMO-CACO finds Pareto optimal solutions through ant-path selection and sampling operations by introducing an accumulated rank for the solutions in each single-objective function into solution sorting to improve learning performance. Simulations are conducted to verify the AMO-CACO-based FCRNN gait generation performance through comparisons with different multiobjective optimization algorithms. Selected software-designed Pareto optimal FCRNNs are then applied to control the gait of a real NAO robot.

关键词： Ant colony optimization (ACO) biped robot gait control central pattern generators (CPGs) multiobjective optimization algorithms recurrent neural networks (RNNs)

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Improving Computing Systems Automatic multiobjective optimization Through Meta-optimization

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2016年第7期35卷 1125-1129页

作者： Vintan, Lucian Chis, Radu Ismail, Muhammad Ali Cotofana, Cristian Lucian Blaga Univ Sibiu Sibiu 550024 Romania Tech Univ Cluj Napoca Cluj Napoca 400020 Romania NED Univ Engn & Technol Karachi 75270 Pakistan

This paper presents the extension of framework for automatic design space exploration (FADSE) tool using a meta-optimization approach, which is used to improve the performance of design space exploration algorithms, by driving two different multiobjective meta-heuristics concurrently. More precisely, we selected two genetic multiobjective algorithms: 1) non-dominated sorting genetic algorithm-II and 2) strength Pareto evolutionary algorithm 2, that work together in order to improve both the solutions' quality and the convergence speed. With the proposed improvements, we ran FADSE in order to optimize the hardware parameters' values of the grid ALU processor (GAP) micro-architecture from a bi-objective point of view (performance and hardware complexity). Using our developed approach we obtained better GAP instances (a configuration has for almost the same cycles per instruction -1.00, the hardware complexity 38% smaller/better -35.81 versus 58.61) in half of the time compared to a classical sequential optimization approach (5 days versus 10 days).

关键词： Design space exploration grid ALU processor (GAP) meta-optimization multiobjective optimization algorithms

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A multiobjective Genetic Algorithm based Hybrid Recommendation Approach

A Multiobjective Genetic Algorithm based Hybrid Recommendati...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wang, Pan Zuo, Xingquan Guo, Congcong Li, Ruihong Zhao, Xinchao Luo, Chaomin Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China Univ Detroit Mercy Dept Elect & Comp Detroit MI 48221 USA

ISBN: (纸本)9781538627266

Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches.

关键词： recommendation algorithms multiobjective optimization algorithms hybrid algorithm

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A multiobjective Genetic Algorithm based Hybrid Recommendation Approach

A Multiobjective Genetic Algorithm based Hybrid Recommendati...

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IEEE Symposium Series on Computational Intelligence

作者： Pan Wang Ruihong Li Xingquan Zuo Xinchao Zhao Congcong Guo Chaomin Luo School of Computer Science Ministry of Education Beijng China School of Science Beijing University of Posts and Telecommunications Beijng China Department of Electrical and Computer Engineering University of Detroit Mercy MI USA

关键词： Recommendation algorithms multiobjective optimization algorithms Hybrid algorithm

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