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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Yin, Xiang Liu, Jian Cheng, Shi Zang, Siqi State Grid Xinjiang Electric Power Co. Ltd. Urumqi830000 China Xinjiang Power Transmission and Transformation Co. Ltd. Urumqi830000 China

ISBN: (纸本)9798350393224

The optimization proportion of distribution network feeder capacity cannot reach the expected standard, so this paper proposes a multi-objective particle swarm optimization algorithm for distribution network feeder capacity considering dynamic load. The multi-objective method is used to expand the planning scope, the multi-objective particle swarm optimization feeder capacity planning matrix is set, and the feeder capacity optimization planning calculation model under dynamic load is designed. The experimental results show that the final optimization rate of the feeder capacity in the distribution network can reach over 5, indicating that the designed directional planning algorithm has high measurement accuracy, strong pertinence, and controllable error under dynamic load conditions and has practical application value. © 2023 IEEE.

关键词： Counting and dynamic load Distribution network Feeder capacity multi-objective particle swarm Optimization planning Planning algorithm

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Hybrid mRMR and multi-objective particle swarm feature selection methods and application to metabolomics of traditional Chinese medicine

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2073-e2073页

作者： Zhang, Mengting Du, Jianqiang Nie, Bin Luo, Jigen Liu, Ming Yuan, Yang Jiangxi Univ Chinese Med Sch Comp Sci Nanchang Jiangxi Peoples R China Jiangxi Univ Chinese Med Key Lab Artificial Intelligence Chinese Med Nanchang Jiangxi Peoples R China

Metabolomics data has high-dimensional features and a small sample size, which is typical of high-dimensional small sample (HDSS) data. Too high a dimensionality leads to the curse of dimensionality, and too small a sample size tends to trigger overfitting, which poses a challenge to deeper mining in metabolomics. Feature selection is a valuable technique for effectively handling the challenges HDSS data poses. For the feature selection problem of HDSS data in metabolomics, a hybrid Max-Relevance and Min-Redundancy (mRMR) and multi-objective particle swarm feature selection method (MCMOPSO) is proposed. Experimental results using metabolomics data and various University of California, Irvine (UCI) public datasets demonstrate the effectiveness of MCMOPSO in selecting feature subsets with a limited number of high-quality features. MCMOPSO achieves this by efficiently eliminating irrelevant and redundant features, showcasing its efficacy. Therefore, MCMOPSO is a powerful approach for selecting features from high-dimensional metabolomics data with limited sample sizes.

关键词： High dimensional small samples Feature selection multi-objective particle swarm Metabolomics

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A multi-objective particle swarm optimization for project selection problem

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第1期37卷 315-321页

作者： Rabbani, M. Bajestani, M. Aramoon Khoshkhou, G. Baharian Univ Tehran Dept Ind Engn Tehran 4563 Iran

Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this Study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multi-objective particle swarm Project selection problem multi-objective genetic algorithm

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A multi-objective particle swarm for a flow shop scheduling problem

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2007年第1期13卷 79-102页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particle's position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.

关键词： bi-criteria flow shop scheduling problem permutation flow shop multi-objective particle swarm multi-objective genetic algorithm

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Designing fuzzy logic controllers for DC-DC converters using multi-objective particle swarm optimization

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ELECTRIC POWER SYSTEMS RESEARCH 2014年 112卷 74-83页

作者： Siano, Pierluigi Citro, Costantino Univ Salerno Dept Ind Engn I-84084 Fisciano SA Italy Univ Politecn Cataluna Dept Elect Engn Barcelona Spain

This paper proposes a new method for designing fuzzy logic controllers for voltage-regulated DC DC power converters. Low sensitivity to input voltage variations, fast regulation during load transients and robustness against the aging process of the converter passive components are the main features achievable through the proposed approach. The multi-objective particle swarm optimization (MO-PSO) is used to find multiple Pareto-optimal solutions in a multi-objective optimization problem for the identification of the best possible membership functions and rules of the fuzzy controller. In order to evaluate the effectiveness of the proposed technique, different cases have been tested in laboratory on a buck converter prototype, considering deep input voltage deviations and big load transients, as well as variations in the nominal values of the passive components of the system. A comparison with a classical proportional-integral (PI) controller demonstrates the high performances of the proposed technique in terms of effective output voltage regulation under different operating conditions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Current-mode control DC-DC converter Fuzzy logic controller multi-objective particle swarm

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Adaptive multiple selection strategy for multi-objective particle swarm optimization

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INFORMATION SCIENCES 2023年 624卷 235-251页

作者： Han, Honggui Zhang, Linlin Yinga, A. Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Minist Educ Beijing 100124 Peoples R China China Natl Heavy Duty Truck Grp Co LTD Automot Res Inst Jinan 250102 Peoples R China Minist Publ Secur Res Inst 1 Beijing 100048 Peoples R China

multiple-swarm approach is a quite successful evolutionary computation framework for multi-objective particle swarm optimization algorithm (MOPSO) to solve multi-objective optimization problems (MOPs). However, the main challenge of using this framework lies in the lack of leader selection, resulting in the optimal solutions being distributed loosely, as well as far away from the true Pareto-optimal front. To overcome this problem, a multi-swarm MOPSO with an adaptive multiple selection strategy (MOPSO-AMS) is investigated in this paper. This proposed MOPSO-AMS is able to guide each swarm with a suitable lea-der to improve the evolutionary performance. The novelties and advantages of MOPSO-AMS include the following three aspects. First, a hierarchical evolutionary state detection mechanism, based on the distribution and dominance information of non-dominated solu-tions, is designed to obtain the evolutionary state of current iteration. Then, the require-ments of evolutionary process can be detected. Second, an adaptive multiple selection strategy, using the evolutionary state information and spatial features of candidate solu-tions, is developed to select leaders of sub-swarms in multiple evolutionary states. Then, suitable leaders can be selected to keep the balance between convergence and diversity. Third, an adaptive parameter adjustment mechanism, based on the dominance relationship of each particle, is introduced to further improve the evolutionary performance of MOPSO-AMS. Finally, numerical simulations and a practical application are used to validate the analytical results and demonstrate the significant improvement of MOPSO-AMS.(c) 2022 Elsevier Inc. All rights reserved.

关键词： multi-objective particle swarm optimization multi-objective optimization problem Adaptive multiple selection strategy Evolutionary state detection

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Complex permittivity extraction of typical wooden furniture materials based on multi-objective particle swarm optimization over 40-50 GHz 18

Complex permittivity extraction of typical wooden furniture ...

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18th European Conference on Antennas and Propagation (EuCAP)

作者： Li, Pan Shao, Yu Xu, Ran Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9788831299091;9798350394436

In order to facilitate the study of indoor propagation characteristics of millimeter waves, it is necessary to extract electromagnetic parameters of indoor materials in the millimeter wave frequency band. It is understood that there is currently limited research on the electromagnetic characteristics of materials with the same material and different internal structures. In the 40-50 GHz frequency band, the free-space method is used to measure three types of materials: plywood, particle wood, and solid wood. To avoid the ripples and ambiguity problems in the common methods, we propose a multi-objective particle swarm optimization scheme to extract the dielectric constant and conductivity of the materials using the measured values and formula calculated values at 30 degrees incidence angle. Comparing with measurements at other incidence angles, the extraction results are validated and analyzed, and the fitting curve shows good results.

关键词： electromagnetic parameters free-space method millimeter wave multi-objective particle swarm

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A multi-objective particle swarm Optimization Algorithm Based on Enhanced Selection

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IEEE ACCESS 2019年 7卷 168091-168103页

作者： Li, Xin Li, Xiao-Li Wang, Kang Li, Yang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Adv Innovat Ctr Future Internet Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Minist Educ Beijing 100124 Peoples R China Commun Univ China Sch Int Studies Beijing 100024 Peoples R China

Most multi-objective particle swarm optimization algorithms, which have demonstrated their good performance on various practical problems involving two or three objectives, face significant challenges in complex problems. For overcoming this challenges, a multi-objective particle swarm optimization algorithm based on enhanced selection(ESMOPSO) is proposed. In order to increase the ability of exploration and exploitation, enhanced selection strategy is designed to update personal optimal particles, and objective function weighting is used to update global optimal particle adaptively. In addition, R2 indicator is incorporated into the achievement scalarizing function to layer particles in archive, which promotes the archive update. Besides, Gaussian mutation strategy is designed to avoid particles falling into local optimum, and polynomial mutation is applied in archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental results demonstrate that ESMOPSO algorithm shows very competitive performance when dealing with complex MOPs.

关键词： multi-objective particle swarm enhanced selection objective function weighting achievement scalarizing function

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A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem

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ENGINEERING OPTIMIZATION 2007年第8期39卷 877-898页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Rabbani, M. Univ Tehran Dept Ind Engn Tehran Iran

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.

关键词： mixed-model assembly line multi-objective sequencing problem multi-objective particle swarm tabu search multi-objective genetic algorithm

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A multi-objective particle swarm optimization algorithm based on two-archive mechanism

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APPLIED SOFT COMPUTING 2022年 119卷

作者： Cui, Yingying Meng, Xi Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Engn Res Ctr Intelligent Percept & Autonomous Con Minist Educ Beijing 100124 Peoples R China

As a powerful optimization technique, multi-objective particle swarm optimization algorithms have been widely used in various fields. However, performing well in terms of convergence and diversity simultaneously is still a challenging task for most existing algorithms. In this paper, a multi-objective particle swarm optimization algorithm based on two-archive mechanism (MOPSO_TA) is proposed for the above challenge. First, two archives, including convergence archive (CA) and diversity archive (DA) are designed to emphasize convergence and diversity separately. On one hand, particles are updated by indicator-based scheme to provide selection pressure toward the optimal direction in CA. On the other hand, shift-based density estimation and similarity measure are adopted to preserve diverse candidate solutions in DA. Second, the genetic operators are conducted on particles from CA and DA to further enhance the quality of solutions as global leaders. Then the search ability of MOPSO_TA can be improved by performing hybrid operators. Furthermore, to balance global exploration and local exploitation of MOPSO_TA, a flight parameters adjustment mechanism is developed based on the evolutionary information. Finally, the proposed algorithm is compared experimentally with several representative multi-objective optimization algorithms on 21 benchmark functions. The experimental results demonstrate the competitiveness and effectiveness of the proposed method.(c) 2022 Elsevier B.V. All rights reserved.

关键词： multi-objective particle swarm optimization Two-archive mechanism Genetic operator Evolutionary information

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