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A new bifuzzy optimization method for remanufacturing scheduling using extended discrete particle swarm optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107219-107219页

作者： Shi, Jiaxuan Zhang, Wenyu Zhang, Shuai Chen, Jie Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

As the energy shortage and environment pollution become increasingly deteriorating, remanufacturing has become a hot research field for its energy-saving and environmental advantages. In remanufacturing systems, scheduling is a key step that directly affects the successful realization of remanufacturing benefits. Since the remanufacturing involves many two-fold uncertainties, scheduling for remanufacturing is more challenging than for traditional manufacturing. However, few studies have addressed these two-fold uncertainties in remanufacturing environments. Thus, this study proposes a new bifuzzy remanufacturing scheduling model, which handles many two-fold uncertainties in remanufacturing and applies the bifuzzy variable to describe these uncertainties. Multiple non-identical parallel processing lines for remanufacturing end-of-life products have also been integrated into the proposed model. An extended discrete particle swarm optimization algorithm with an effective representation scheme is proposed to solve the model. In the presented algorithm, three new guiding directions and a new multi-directional guiding strategy are designed to enhance the diversity of population, and a new position updating mechanism, local search strategy, and anti-stagnation mechanism are integrated to improve the algorithmic performance. Experiments are performed to verify the effectiveness of the proposed model by comparing it with the traditional deterministic model, and the effectiveness of the proposed algorithm in solving this model by comparing it with other baseline algorithms.

关键词： Remanufacturing scheduling Bifuzzy theory Bifuzzy variable Multiple non-identical parallel processing lines particle swarm optimization algorithm

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Improved subgroup method coupled with particle swarm optimization algorithm for intra-pellet non-uniform temperature distribution problem

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ANNALS OF NUCLEAR ENERGY 2021年 153卷 108070-108070页

作者： Li, Song Zhang, Qian Zhang, Zhijian Zhao, Qiang Liang, Liang Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Coll Nucl Sci & Technol 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

Due to the Doppler broadening effect, the conventional subgroup weights of different temperatures correspond to different energy ranges. Therefore, the conventional subgroup fixed source equation could not be established without approximation between different temperatures. In this study, the improved subgroup method coupled with the particle swarm optimization algorithm (ISPSO) is proposed. The subgroup fixed source equation is solved for different temperatures by sharing the same set of subgroup weights, which are calculated by generationally updating the particle position during the particle swarm optimization. Afterward, the partial subgroup levels are obtained by the least square method. Besides, ISPSO adopts the fine mesh structure for the resonance interference treatment, then the multigroup effective resonance cross section is obtained by the group condensation. The subgroup parameters of ISPSO could be either generated online or pre-determined. The numerical verification indicates that ISPSO could accurately handle the resonance treatment for the complicated temperature conditions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Resonance self-shielding Subgroup method Subgroup parameters Non-uniform temperature distribution particle swarm optimization algorithm

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optimization of dividing wall columns based on online Kriging model and improved particle swarm optimization algorithm

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COMPUTERS & CHEMICAL ENGINEERING 2022年 166卷

作者： Liang, Mengkun Song, Jiayin Zhao, Kefan Jia, Shengkun Qian, Xing Yuan, Xigang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Tianjin Univ Chem Engn Res Ctr Collaborat Innovat Ctr Chem Sci & Engn Tianjin Sch Chem Engn & Technol Tianjin 300350 Peoples R China Zhejiang SUPCON Technol Co Ltd Hangzhou 310053 Peoples R China

Dividing wall columns (DWCs) can effectively improve the thermodynamic efficiency of traditional distillation columns. However, DWCs have intricate structures and strong internal interactions. Numerous structural and operational variables are interrelated. This work presents an improved cellular particle swarm optimization based on the online Kriging model (KCPSO) algorithm, and applies it to the optimization of DWC with the objective of minimizing the total annual cost. The algorithm uses the information of particle swarm search to act on the online Kriging model, and reacts on the particle search through the information of the online Kriging model. Calculations demonstrate that the KCPSO algorithm is superior to standard particle swarm optimization (PSO) and cellular PSO (CPSO) algorithms due to its higher quality of iteration. The KCPSO algorithm can effectively overcome the difficulty of early convergence of the CPSO algorithm and the problem that the PSO algorithm is prone to falling into local optimal solutions.

关键词： Heuristic algorithm Online surrogate model Optimal design Dividing wall columns particle swarm optimization algorithm

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Design of Course Scheduling System Based on particle swarm optimization algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2020年第8期6卷 234-244页

作者： Li Wang Zhuwen Tang Wei Wang

Reasonable and efficient course scheduling system is essential to improve the efficiency of university teaching management. The course scheduling problem is a combination optimization problem. It needs to allocate time and classrooms reasonably, meet specific constraints and avoid conflicts. In this paper, by analyzing the various factors and constraints involved in the course scheduling problem, a mathematical model of the course scheduling problem is constructed, and the realization process and steps of the course scheduling system design based on particle swarm optimization are given. Based on MATLAB software for simulation design and testing, the results of automatic scheduling are given from the three perspectives of class, teacher, and classroom. The results show that the designed scheduling system can meet the constraint relationship between teachers, classrooms, and courses, and can improve utilization of low-level classrooms.

关键词： Course Scheduling System Constraints Mathematical Model particle swarm optimization algorithm Simulation Results

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Regional-modal optimization problems and corresponding normal search particle swarm optimization algorithm

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swarm AND EVOLUTIONARY COMPUTATION 2023年 78卷

作者： Yuan, Yi-li Hu, Chang-ming Li, Liang Mei, Yuan Wang, Xue-yan Xian Univ Architecture & Technol Coll Civil Engn Xian 710055 Shanxi Peoples R China Xian Univ Architecture & Technol Shaanxi Key Lab Geotech & Underground Space Engn Xian 710055 Shaanxi Peoples R China Xian Polytech Univ Coll Urban Planning & Municipal Engn Xian 710048 Shaanxi Peoples R China

Moavattd by me limit state auve kLSC) finding problem in reliability analysis. a new categmy of optinuyation problems refened to as the regional-modal optimization problems (RMOPs) was investigated in this paper. The most distinguishing feature of RMOPs is the continuity of its solutions. E-dsting optimization methods are not capable of capturing this feature and thus cannot produce acceptable results for RMOPs. Therefore, based on niching PSO a normal search particle swarm optimization (NSPSO) algorithm that can provide discrete optimal solutions for a continuous optimal region with arbitrary pie-specified density was developed. NSPSO is consisting of a normal search pattern and a multi-strategy fusion. Nomial searching is the core of NSPSO, in which each particle is guided by the normal vector of the region composed of its several best neighborhoods. Normal searching prevents the panicles from clustering and thus provide a basis for the solution diversity. Further, a multi strategy framework with three components was introduced to improve the performance of NSPSO. It inchides a dynamic particle repulsion strategy that improve the solution diversity, a panicle memory strategy to prevent local optimum, and an elite breeding strategy that was developed to increase the efficiency of the method. This framework gives NSPSO the ability to maintain the balance between exploitation and e ploration and thereby to realize the uniform disnibution and high coverage and diversity of the algorithm. Furthermore, the key parameters involved in NSPSO were analyzed thoroughly. The NSPSO is compared with ten state-of-theart multi-mode optimization algorithms in terms of twenty typical test functions with different properties that were constructed in this study. The experimental results demonstrate the superiority of our proposed algorithm over the state-of-the-art algorithms in solving RMOPs.

关键词： Regional-modal optimization problems particle swarm optimization algorithm Normal search Dynamic particle repulsion particle memory Density-based elite breeding

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Design and optimization of a TiO2/RGO-supported epoxy multilayer microwave absorber by the modified local best particle swarm optimization algorithm

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NANOTECHNOLOGY REVIEWS 2023年第1期12卷

作者： Karami, Mohammad Reza Jaleh, Babak Eslamipanah, Mahtab Nasri, Atefeh Rhee, Kyong Yop Bu Ali Sina Univ Fac Sci Dept Phys Hamadan 65174 Iran Kyung Hee Univ Dept Mech Engn Yongin 17104 South Korea

Microwave absorbers have many applications in medical, industrial, and military devices. Polymeric composites including carbon-based filler can be used as lightweight absorbers with high electromagnetic (EM) wave absorption performance. Hence, multilayer microwave absorbers were designed using titanium dioxide (TiO2)/reduced graphene oxide (RGO)/epoxy nanocomposites with different weight percentages manufactured using refluxing and annealing methods. The characterization of nanocomposite indicated thin layers of TiO2/RGO as divided sheets in epoxy. The EM properties of the nanocomposites were examined using the Nicolson-Ross-Weir (NRW) detection method. The S-parameters were measured using PNA-N5222A Microwave Network Analyzer. The multilayer absorber software was designed based on the modified local best particle swarm optimization algorithm by MATLAB software, in which the material and thickness of layers were optimized with two cost functions in X-band frequencies. The first cost function seeks to reach the best absorption bandwidth, and the second cost function seeks to reach the maximum average return loss (RL) of the frequency range of 8.2-12.4 GHz. A maximum bandwidth with an RL of less than -12.81 dB was obtained with a thickness of 2.4 mm. A maximum average RL of -22.1 dB was obtained with a thickness of 2.6 mm. The maximum absorption peak was observed with a thickness of 2.5 mm with -62.82 dB at a frequency of 10.86 GHz.

关键词： graphene oxide TiO2 nanoparticles multilayer microwave absorber particle swarm optimization algorithm

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Effects of Random Values for particle swarm optimization algorithm

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algorithmS 2018年第2期11卷 23-23页

作者： Dai, Hou-Ping Chen, Dong-Dong Zheng, Zhou-Shun Cent South Univ Sch Math & Stat Changsha 410083 Hunan Peoples R China Jishou Univ Sch Math & Stat Jishou 416000 Peoples R China State Key Lab High Performance Complex Mfg Changsha 410083 Hunan Peoples R China

particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [1, 1], and Gauss distribution with mean 0 and variance 1 (U [0, 1], U [1, 1] and G (0, 1)), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [1, 1] or G (0, 1) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [1, 1] or G (0, 1) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

关键词： particle swarm optimization algorithm random values uniform distribution gauss distribution

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A new boredom-aware dual-resource constrained flexible job shop scheduling problem using a two-stage multi-objective particle swarm optimization algorithm

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INFORMATION SCIENCES 2023年第1期643卷

作者： Shi, Jiaxuan Chen, Mingzhou Ma, Yumin Qiao, Fei Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China Tongji Univ Sch Econ & Management Shanghai 201804 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

Dual-resource constrained flexible job shop scheduling problem has become a hot research field in recent years. However, few studies have considered workers' boredom sensations when allo-cating resources and scheduling tasks, leading to inappropriate task allocation and negative effects such as efficiency reductions and absenteeism. Therefore, a new boredom-aware dual -resource constrained flexible job shop scheduling problem is investigated in this study, which considers the increase in workers' boredom caused by repetitive job assignments and constructs an efficiency function to characterize the impact of workers' boredom. For this problem, a bi-level lexicographic model, which takes the effective completion of all manufacturing tasks as the primary optimization objective and higher worker job satisfaction as the secondary optimization objective, is established. A two-stage multi-objective particle swarm optimization algorithm with a three-dimensional representation scheme is presented to solve this model. In this algorithm, a new position-updating mechanism and a local search strategy are devised to effectively evolve the solution, and a decomposition strategy with a self-adaptive neighborhood size and a boundary exploration mechanism are embedded to obtain a uniformly distributed Pareto front. Experi-mental results confirm the superiority of the presented model and algorithm.

关键词： Dual-resource constrained flexible job shop scheduling problem Workers ' boredom sensations Lexicographic method Two-stage multi -objective optimization algo rithm particle swarm optimization algorithm

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Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107924-107924页

作者： Li, Yue Chu, Xiaoquan Tian, Dong Feng, Jianying Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum;thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation. (C) 2021 Elsevier B.V. All rights reserved.

关键词： K-means clustering algorithm particle swarm optimization algorithm Adaptive parameter learning Mixed data Customer segmentation

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Concentration Retrieval in a Calibration-Free Wavelength Modulation Spectroscopy System Using particle swarm optimization algorithm

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SENSORS 2023年第14期23卷 6374-6374页

作者： Zhang, Tingting Sun, Yongjie Wang, Pengpeng Zhu, Cunguang Liaocheng Univ Sch Phys Sci & Informat Technol Liaocheng 252000 Peoples R China Univ Jinan Sch Phys & Technol Jinan 250022 Peoples R China

This paper develops a concentration retrieval technique based on the particle swarm optimization (PSO) algorithm, which is used for a calibration-free wavelength modulation spectroscopy system. As compared with the commonly used technique based on the Levenberg-Marquardt (LM) algorithm, the PSO-based method is less dependent on the pre-characterization of the laser tuning parameters. We analyzed the key parameters affecting the performance of the PSO-based technique and determined their optimal parameter values through testing. Furthermore, we conducted a comparative analysis of the efficacy of two techniques in detecting C2H2 concentration. The results showed that the PSO-based concentration retrieval technique is about 63 times faster than the LM-based one in achieving the same accuracy. Within 5 s, the PSO-based technique can produce findings that are generally consistent with the values anticipated.

关键词： particle swarm optimization algorithm wavelength modulated spectroscopy calibration-free spectral fitting

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