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Chinese Automation Congress (CAC)

作者： Cui, Yingying Qiao, Junfei Meng, Xi Beijing Univ Technol Fac Informat Technol Beijing Key Lab Computat Intelligence & Intellige Beijing Peoples R China

ISBN: (纸本)9781728176871

In the past few decades, many variations of multiobjective particle swarm optimization algorithm have been proposed to balance the convergence and diversity of optimal solutions. However, it is still difficult to obtain a set of accurate and well-distributed solutions for most complicated multiobjective problems. In this work, we present a multi-stage multiobjective particle swarm optimization algorithm (MSMOPSO) based on the evolutionary information of population. The evolutionary information of population is evaluated by the particle evolutionary ability, the population evolutionary ability, and the particle evolutionary efficiency. On the one hand, the optimization process is divided into two stages according to the population evolution information. Then, a novel leader selection strategy and a mutation operator to the particles are applied in different stages, aiming to improve the convergence performance and population diversity separately. On the other hand, flight parameters including the inertia weights and learning factors are adjusted adaptively and independently, which can better balance the global exploration and local exploitation abilities of the algorithm. Finally, the proposed algorithm is evaluated on benchmark test functions. And results show that the MSMOPSO is highly competitive for its obtained approximate Pareto fronts with uniform distribution and satisfactory convergence.

关键词： multi-objective particle swarm optimization evolutionary information flight parameters mutation operator

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DMOPSO: Dual multi-objective particle swarm optimization

DMOPSO: Dual Multi-Objective Particle Swarm Optimization

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IEEE Congress on Evolutionary Computation (CEC)

作者： Lee, Ki-Baek Kim, Jong-Hwan Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 305701 South Korea

ISBN: (纸本)9781479914883

Since multi-objective optimization algorithms (MOEAs) have to find exponentially increasing number of nondominated solutions with the increasing number of objectives, it is necessary to discriminate more meaningful ones from the other nondominated solutions by additionally incorporating user preference into the algorithms. This paper proposes dual multi-objective particle swarm optimization (DMOSPO) by introducing secondary objectives of maximizing both user preference and diversity to the nondominated solutions obtained for primary objectives. The proposed DMOSPO can induce the balanced exploration of the particles in terms of user preference and diversity through the dual-stage of nondominated sorting such that it can generate preferable and diverse nondominated solutions. To demonstrate the effectiveness of the proposed DMOPSO, empirical comparisons with other state-of-the-art algorithms are carried out for benchmark functions. Experimental results show that DMOPSO is competitive with the other compared algorithms and properly reflects the user's preference in the optimization process while maintaining the diversity and solution quality.

关键词： multi-objective Evolutionary Algorithm multi-objective particle swarm optimization Dual-stage dominance check Crowding distance User preference

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Research on optimization of City Line Express Trains Based on multi-objective particle swarm optimization Algorithm 4

Research on Optimization of City Line Express Trains Based o...

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4th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Hao, Li Luo, Qin Shenzhen Univ Coll Urban Rail Transit Shenzhen Peoples R China Shenzhen Technol Univ Coll Urban Transportat & Logist Shenzhen Peoples R China

ISBN: (纸本)9781728145532

The municipal line is the route connecting the suburbs of the city and the city center. Many passengers need to travel long distances. The traditional rail transit train station stop form can not meet the needs of passengers well. Therefore, this paper adopts the method of fast and slow car operation. A multi-objective optimization model was established based on passenger travel time and operating cost of the enterprise, and a multi-objective optimized particle swarm optimization algorithm was designed to solve the problem. In the study of the example, the corresponding express train plan was designed for the Shenzhen Metro Line 14, which proved that the travel speed of the passenger train reduced the travel time of the passengers and the operating cost of the enterprise, and verified the reliability of the model and algorithm.

关键词： urban rail transit multi-objective optimization fast and slow vehicle multi-objective particle swarm optimization

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Global Localization of Monte Carlo Localization Based on multi-objective particle swarm optimization 6

Global Localization of Monte Carlo Localization Based on Mul...

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6th IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Chien, Chiang-Heng Hsu, Chen-Chien Wang, Wei-Yen Kao, Wen-Chung Chien, Chiang-Ju Natl Taiwan Normal Univ Dept Elect Engn Taipei Taiwan Huafan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781509020966

Premature convergence often happens when a Monte Carlo localization (MCL) algorithm tries to localize a robot under highly symmetrical environments. In this paper, we propose a novel method of solving such problem for global localization by incorporating a multi-objective evolutionary approach to resample particles with two objectives, including particle weights and population distribution. By employing a multi-objective particle swarm optimization (MOPSO), our approach is capable of enhancing the exploration ability to improve population diversity while maintaining convergence quality to successfully localize the global optima. Simulation results have confirmed that localization performance using the proposed approach is significantly improved.

关键词： Premature Convergence Monte Carlo localization multi-objective particle swarm optimization Global Localization.

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Parameters Estimation in Topological Kernel Bayesian ART using multi-objective particle swarm optimization 8

Parameters Estimation in Topological Kernel Bayesian ART usi...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Lee, Gin Chong Loo, Chu Kiong Masuyama, Naoki Multimedia Univ Fac Engn & Technol Melaka Malaysia Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur Malaysia Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Osaka Japan

ISBN: (纸本)9781538692769

Potentials in Topological Kernel Bayesian Adaptive Resonance Theory (TKBA) are advocated by the data specific parameters: kernel bandwidth sigma(cim) in correntropy induced metric (CIM) and kernel bandwidth sigma(kbr) in kernel Bayes' rule (KBR). This paper proposes a new calibration mechanism to implement multi-objective particle swarm optimization (MOPSO) for parameters estimation in TKBA with the intention of searching for optimal values of parameters sigma(cim) and sigma(kbr). Calibration mechanism is designed based on the measure of robustness, adaptability and the quality of the learned topological network. Two case studies has been empirically carried out using UCI real world dataset. Experiment results in case study I provide proof-of-concept of the proposed calibration mechanism. Case study II compares MOPSO calibrated TKBA with manual calibrated TKBA in terms of classification performance. Experiment results shows that MOPSO calibrated TKBA is able to enhance the capabilities of TKBA.

关键词： multi-objective particle swarm optimization topological kernel Bayesian adaptive resonance theory kernel Bayes' rule correntropy induced metric

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Credible Web Service Composition based on Improved multi-objective particle swarm optimization 40

Credible Web Service Composition based on Improved Multi-obj...

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40th Chinese Control Conference (CCC)

作者： Liu, Feng Han, Min Liu, Jianwei Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China

ISBN: (纸本)9789881563804

In the field of Web service composition, since the published value of Quality of Service (QoS) is not necessarily reliable, the research on the credibility of Web services has become a research hotspot. However, the current credibility research cannot well describe the difference between the published value and the actual operating value. At the same time, the algorithm used to solve the Web service composition scheme generally has the problem of poor solution effect. Based on this, this paper proposes a credible Web service composition method based on improved multi-objective particle swarm optimization (IMOPSO). Firstly, this paper proposes a similarity-based credibility calculation model, and combines it with the QoS published value through the inverse of the similarity, so that the QoS of the Web service meets the actual operation effect. Secondly, this paper proposes an improved multi-objective particle swarm optimization algorithm, which solves the problem of premature convergence by expanding the population diversity, and the solution effect is significantly improved. Finally, the improved multi-objective particle swarm algorithm is used to solve the Web service composition problem based on credible QoS. Simulation experiments on the QWS_V2 data set and the Synthetic data set prove that the method proposed in this paper can effectively improve the credibility of the service composition scheme, and has higher composition efficiency and better composition effect.

关键词： credibility Web service composition QoS multi-objective particle swarm optimization

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An improved multi-objective particle swarm optimization algorithm based on quantum behavior for an ethylene cracker 38

An improved multi-objective particle swarm optimization algo...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Lin, Xiaoyong Wang, Xinlu Chen, Zhiwei Geng, Zhiqiang Han, Yongming Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (纸本)9798350303636

The multi-objective particle swarm optimization based on quantum behavior algorithm (MOQPSO) has a great improvement in performance compared with the conventional PSO-based approach for optimizing multiple objectives, but the MOQPSO still has the disadvantages of low global convergence ability and prone to getting trapped in local optima during optimization in the late period. Therefore, an enhanced particle swarm method for optimizing multiple objectives based on quantum behavior algorithm (IMOQPSO) is proposed. Based on the improved delta well model, the probability of particles appearing far away from the central point is increased, the particle search scope is expanded, and the proposed algorithm exhibits enhanced global convergence capabilities. At the same time, the triple dynamic switching of characteristic length improves the adaptability of the proposed algorithm. Finally, the IMOQPSO algorithm is compared with other prevalent multi-objective optimization methods on the test function, and the results are compared to verity that the IMOQPSO has better capability to converge globally and aptitude for local exploration. Furthermore, both the IMOQPSO and the reference algorithm are employed to optimize the ethylene cracking furnace model, using ethylene yield and propylene yield as the target objectives. The results show that the IMOQPSO achieves the best results compared with the initial operational parameters during the optimization process of the main product yield under the fixed cycle regulation operating conditions. © 2023 IEEE.

关键词： delta potential well Ethylene cracking furnace multi-objective particle swarm optimization Quantum behavior Triple dynamic switching Wave function

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Robust Design optimization Based on multi-objective particle swarm optimization

Robust Design Optimization Based on Multi-Objective Particle...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Yu Yan Dai Guangming Chen Liang Zhou Chong Peng Lei China Univ Geosci Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781509006229

For real world problems, there are inevitably perturbations in the design parameters or (and) variables. If an optimal solution is sensitive to the small perturbations of design parameters or variables, it may be inappropriate or risk for practical use. Robust design optimization can find solutions which are good in optimality and good in robustness simultaneously. Traditional robust optimization searched for robust solution by converting the original problem into a single-objective optimization problem. But only one solution can be obtained from one run of optimization using these methods. This paper applied a multi-objective optimization approach to get the robust optimal solutions. A novel robustness measurement is proposed and is compared with other methods of estimating robustness. The theoretical and experimental results verify that the proposed method outperforms the conventional methodologies. In solving the converted multi-objective optimization problems, a more efficient multi-objective particle swarm optimization is used. The test functions proved that the proposed method is more efficient than the conventional ones.

关键词： perturbations robust design optimization robustness measurement multi-objective particle swarm optimization

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Data Analytics-Based multi-objective particle swarm optimization for Determination of Congestion Thresholds in LV Networks

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ENERGIES 2019年第7期12卷 1295-1295页

作者： Leiva, Javier Carmona Pardo, Ruben Aguado, Jose A. Endesa Madrid 28042 Spain Univ Malaga Dept Elect Engn Malaga 29016 Spain

A growing presence of distributed energy resources (DER) and the increasingly diverse nature of end users at low-voltage (LV) networks make the operation of these grids more and more challenging. Particularly, congestion and voltage management strategies for LV grids have usually been limited to some elemental criteria based on human experience, asset oversizing, or grid reinforcement. However, with the current massive deployment of sensors in modern LV grids, new approaches are feasible for distribution network assets operation. This article proposes a multi-objective particle swarm optimization (MOPSO) approach, combined with data analytics through affinity propagation clustering, for congestion threshold determination in LV grids. A real case study from the smart grid of Smartcity Malaga Living Lab is used to illustrate the proposed approach. Within this approach, distribution system operators (DSOs) can take decisions in order to prevent situations of risk or potential failure at LV grids.

关键词： congestion management low-voltage networks multi-objective particle swarm optimization affinity propagation clustering optimal congestion threshold

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A new metric for optimal visual comfort and energy efficiency of building lighting system considering daylight using multi-objective particle swarm optimization

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JOURNAL OF BUILDING ENGINEERING 2021年 43卷

作者： Wagiman, Khairul Rijal Abdullah, Mohd Noor Hassan, Mohammad Yusri Radzi, Nur Hanis Mohammad Ind Training Inst Selandar Jalan Batang Melaka Selandar 77500 Melaka Malaysia Univ Tun Hussein Onn Malaysia UTHM Fac Elect & Elect Engn Green & Sustainable Energy GSEnergy Focus Grp Batu Pahat 86400 Johor Malaysia Univ Teknol Malaysia UTM Fac Engn Ctr Elect Energy Syst CEES Sch Elect Engn Johor Baharu 81310 Johor Malaysia

In the past, an optimization-based control strategy of lighting in a building only focused on maximizing energy efficiency in which the results of the visual comfort of occupants is decreased at the minimum level. Therefore, a visual comfort metric model that integrates the artificial lighting and daylighting is crucial to be developed, so that the model can be simultaneously treated with energy performance model in the control strategy for equal performance of energy and visual comfort. This paper proposes a new visual comfort metric model called illuminance uniformity deviation index (IUDI) that reflects illuminance uniformity and the index is considered as an objective function in the optimization-based control, based on a simulation-based study. The proposed IUDI is optimized simultaneously with power demand using multi-objective particle swarm optimization (MOPSO) to find the best compromise solution. The IUDI with MOPSO is implemented in an actual office room by considering mixed artificial light and daylight. Based on the comparative results with the coefficient of variation of root mean square error (CVRMSE), the proposed IUDI showed superior performance in two aspects: (1) technical - improved 6% and 27% of visual comfort and energy savings, respectively and (2) economic - reduced a simple payback period of 28% and a net present value of 33%. The proposed IUDI is a great tool for a new visual comfort metric that satisfies the EN 12464-1 (illuminance level is 500 lux and uniformity is 0.6). Moreover, it also has a practical value to be implemented in green buildings.

关键词： Daylight-linked control Energy savings multi-objective particle swarm optimization optimization-based control Visual comfort metrics

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