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An efficient optimization algorithm that hybridizes DFTB and DFT theories both operated within the modified basin hopping method

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COMPUTER PHYSICS COMMUNICATIONS 2019年 236卷 164-175页

作者： Lai, S. K. Maftuhin, Wafa Natl Cent Univ Dept Phys Complex Liquids Lab Chungli 320 Taiwan Sampoerna Univ Fac Engn & Technol Jakarta Indonesia

The density functional tight-binding (DFTB) theory and density functional theory (DFT) are separately combined with the modified basin hopping (MBH) method into a two-stage optimization algorithm that is then applied in sequence to find the lowest-energy structures of Au clusters. In the first-stage, the DFTB/MBH is conducted mainly to provide a speedy and yet semi-quantitatively reliable searching of the lowest-energy structures so as to pave the way for the second-stage DFT/MBH for more refined and accurate sorting of them at the DFT level. To ensure high efficiency in searching the minimized energy, we pay more attention to the DFTB theory in particular the repulsive part energy and examine the quality of the DFTB parameters fitted to different sets of reference structures. This parametrization study is physically relevant since the cluster's structure is basically determined, by and large, by the repulsive potential. To appreciate the nicety of the present method, we apply the latter and two other existing sets of DFTB parameters to perform the first-stage DFTB/MBH but, separately, we continue the second-stage optimization all by the same DFT/MBH method. We found that the lowest-energy geometries of gold clusters obtained are independent of the use of the DFTB theory to calculate the energy function in the first-stage minimization. Only the efficacy of executing the second-stage DFT/MBH which is generally time-consuming differs, however. In general, for duly-fitted DFTB parameters less computer time is required at the second-stage operation. On comparing further our calculated optimized Au structures with other theoretical calculations and existing experimental clusters, the very good agreement among them clearly explains that the present hybridized scheme has greater potential to efficiently perform with less or within affordable computing time the high-level OFT calculation for medium to larger sized clusters. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Density functional tight-binding theory Density functional theory Metallic cluster optimization algorithm

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A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2017年第1期7卷 1页

作者： Moon, YoungJu Yu, HeonChang Gil, Joon-Min Lim, JongBeom Korea Univ Dept Comp Sci & Engn Seoul South Korea Catholic Univ Daegu Sch Informat Technol Engn Gyeongbuk South Korea Korea Polytech Univ Dept Game & Multimedia Engn Shihung Gyeonggi Do South Korea

Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.

关键词： Task scheduling Ant colony system optimization algorithm Cloud computing

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Forecast of the hydropower generation under influence of climate change based on RCPs and Developed Crow Search optimization algorithm

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ENERGY REPORTS 2021年 7卷 385-397页

作者： Huangpeng, Qizi Huang, Wenwei Gholinia, Fatemeh Univ Def Technol Coll Art & Sci Changsha 410072 Hunan Peoples R China Univ Def Technol Coll Mil Basic Educ Changsha 410072 Hunan Peoples R China Univ Mohaghegh Ardabili Fac Agr & Nat Resources Ardebil Iran

The world's climate has changed dramatically in recent years due to the development of the industry. Climate change can significantly affect hydropower plants. One of the negative effects of climate change is the reduction of hydropower generation. Therefore, to better management of power supply and demand, the climate change effect on hydropower plants should be examined. The main purpose of this study is the prediction of future hydropower generation (2021-2050) in terms of climate change. One of the factors sensitive to climate change in hydropower plants is the amount of input flow to the reservoir. In this study, a method has been used that can increase the accuracy of flow estimation. This innovation is the use of the ANN model under the new version of the Developed Crow Search algorithm (DCSA). This algorithm increases the accuracy of prediction by solving optimization disadvantages such as getting stuck in the optimal location, the imbalance between exploitation and exploration at different levels The results showed that the DCSA algorithm with minimum error (MSE = 1.06) and maximum correlation (R-2 = 0.88) compared to other algorithms has the best performance. The results of the prediction of climate change under RCPs scenarios showed that the average annual power generation will decrease under RCP2.6 and RCP4.5 and RCP8.5 about 10.74% and% 16.38 and 22.25% respectively. Also, the average annual power generation under scenarios RCP2.6, RCP4.5, and RCP8.5 by 2050 are 740.33 MW, 603.12 MW, and 585.77 MW, respectively. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Climate change Energy Forecasting optimization algorithm Hydropower

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A method combining optimization algorithm and inverse-deformation design for improving the injection quality of box-shaped parts

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第3-4期130卷 1969-1985页

作者： Zhai, Haorui Li, Xiaodong Xiong, Xin Zhu, Wuwei Li, Chuqing Wang, Yongqing Chang, Ying Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Mat Sci & Engn Dalian 116024 Peoples R China Yancheng Inst Technol Sch Automot Engn Yancheng 224051 Peoples R China Sunway Ind Zhuhai Co Ltd Zhuhai 519000 Peoples R China

Volume shrinkage and warpage deformation are very critical quality indicators in the plastic injection molding (PIM) of box-shaped thin-walled plastics. These two performance indexes are greatly affected by the molding parameters. Therefore, in this paper, six optimization algorithms and inverse-deformation designs (IDD) are used to reduce volume shrinkage and warpage deformation. Firstly, six important molding parameters, namely filling time (A), plasticity temperature (B), mold temperature (C), holding time (D), maximum holding pressure (E) and cooling time (F), are determined, and the L25 (56) orthogonal experimental design (OED) is established. Taguchi grey correlation (TGC) theory analysis is used to determine the optimal combination of molding parameters. Secondly, different combinations of Box-Behnken design (BBD) response surface method, BP neural network (BPNN) training, and NSGA-II genetic algorithms are used to generate four combined optimization algorithms, in order to perform multi-objective optimization of the six molding parameters. The result shows that the effectiveness of four optimization analyses are ranked as follows: BPNN-BBD-NSGA-II > BPNN-BBD > BBD-NSGA-II > BBD. The BPNN-BBD-NSGA-II method holds the best prediction results. Finally, a global optimization platform based on NX/Moldex3D is established considering the IDD theory to simulate the molding process. Optical scanning instruments are used to examine the molding quality. The result proves that the warpage deformation in box-shaped thin-walled injection-molded products is almost completely eliminated and a high molding quality can be achieved. This research is favorable for designing the molding process and guiding the molding of box-shaped thin-walled injection-molded products.

关键词： Plastic injection molding Volume shrinkage Warpage deformation optimization algorithm Multi-objective optimization Inverse-deformation design

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Stochastic Dual Simplex algorithm: A Novel Heuristic optimization algorithm

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第5期51卷 2725-2734页

作者： Zandavi, Seid Miad Chung, Vera Yuk Ying Anaissi, Ali Univ Sydney Fac Engn & Informat Technol Sch Comp Sci Camperdown NSW 2006 Australia

A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on simplex techniques. A dual simplex is distributed stochastically in the search space to find the best optimal point. Simplexes share the best and worst vertices of one another to move better through search space. The proposed algorithm is applied to 25 well-known benchmarks, and its performance is compared with grey wolf optimizer (GWO), particle swarm optimization (PSO), Nelder-Mead simplex algorithm, hybrid GWO combined with pattern search (hGWO-PS), and hybrid GWO algorithm combined with random exploratory search algorithm (hGWO-RES). The numerical results show that the proposed algorithm, called stochastic dual simplex algorithm (SDSA), has a competitive performance in terms of accuracy and complexity.

关键词： optimization Heuristic algorithms Benchmark testing Cybernetics Particle swarm optimization Genetic algorithms Search problems Heuristic approach optimization algorithm simplex techniques stochastic dual simplex algorithm (SDSA)

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An Agglomerative Greedy Brain Storm optimization algorithm for Solving the TSP

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IEEE ACCESS 2020年 8卷 201606-201621页

作者： Wu, Changyou Fu, Xisong Shandong Inst Business & Technol Sch Management Sci & Engn Yantai 264005 Peoples R China

The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this article, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this article consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.

关键词： Brain storm optimization algorithm traveling salesman problem hierarchical clustering optimization algorithm combinatorial optimization

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Multiobjective optimization Design of Unequal Halbach Array Permanent Magnet Vernier Motor Based on optimization algorithm

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第5期58卷 6014-6023页

作者： Chen, Qiushuo Fan, Ying Lei, Yutong Wang, Xu Southeast Univ Nanjing 210096 Peoples R China

This article proposes a consequent-pole unequal Halbach vernier wheel motor (CPUHVWM), which effectively reduces the self-flux leakage between permanent magnets, and an optimization method is proposed to improve the motor optimization efficiency and shorten the optimization time. The key variables are selected by the sensitivity analysis, and a polynomial function suitable for the optimization algorithm is constructed based on the response surface method. Based on the particle swarm optimization algorithm, the weights of different particle search areas are assigned to improve the overall optimization efficiency of the algorithm. Furthermore, to verify the superiority of the performance of the proposed structure and the applicability of the algorithm, the other two similar motor structures are compared, and the algorithm is adopted for the optimization. The optimized electromagnetic characteristics are compared by the finite element method. Finally, the prototype with the optimal parameters is built, and the effectiveness and advantages of the optimization algorithm are verified by experiments.

关键词： optimization Wheels Torque Rotors Brushless DC motors Permanent magnet motors Stator windings Consequent-pole unequal Halbach vernier wheel motor (CPUHVWM) electromagnetic characteristics optimization algorithm response surface method (RSM) sensitivity analysis

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Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2024年第6期21卷

作者： Guan, Xiaoqing Hu, Tao Zhang, Ziang Wang, Yixu Liu, Yifan Wang, You Hao, Jie Li, Guang Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Rotunbot Hangzhou Technol Co Ltd Hangzhou Peoples R China

The model predictive control trajectory planner is a popular and effective robot local motion planner. However, it is challenging to satisfy real-time requirements and implement them on embedded platforms due to their high complexity of solving and reliance on optimization solvers. This letter reports a lightweight and efficient two-stage solving algorithm for the model predictive control planner. Firstly, the general form of the model predictive control local planning problem was specified and simplified by the motion primitives. Then, a two-stage solving method of multi-layer perceptron pre-solving and particle swarm optimization re-optimizing is developed after splitting the cost function into two pieces. An multi-layer perceptron neural network was designed and trained offline to learn the solution of the model predictive control local planner without considering obstacles after selecting the inputs and outputs. Next, to accomplish obstacle avoidance, the particle swarm optimization algorithm re-optimizes the trajectory based on the outputs of the neural network. The experiment results demonstrate that the multi-layer perceptron-particle swarm optimization algorithm can quickly and accurately solve local planning problems, guiding robots to complete global paths with the same efficiency as expert solvers. The average solving time has been reduced by over 90%, enabling the robot to increase its control frequency or adopt higher-quality complex motion primitives. The multi-layer perceptron-particle swarm optimization algorithm can also be used for various robots and motion primitives, with a wide range of potential applications.

关键词： optimization algorithm model predictive control local planner motion planning multi-layer perceptron particle swarm optimization

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Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

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OPTICAL ENGINEERING 2018年第10期57卷 1-7页

作者： Lei, Xiangxiao Ouyang, Honglin Xu, Lijuan Hunan Univ Coll Elect & Informat Engn Changsha Hunan Peoples R China Changsha Social Work Coll Sch Elect Informat Engn Changsha Hunan Peoples R China

To improve the timeliness of the three-dimensional (3-D) maximum entropy method, an image segmentation method based on equivalent 3-D entropy and artificial fish swarm optimization algorithm is proposed. An equivalent 3-D entropy method without logarithmic operation is developed, and its equivalence is proved theoretically. The optimal threshold is determined based on the artificial fish swarm optimization algorithm so as to avoid exhaustive search and improve algorithm efficiency. The experimental results demonstrate that the proposed method is more time-efficient than the traditional 3-D entropy method and the equivalent 3-D entropy method without affecting segmentation. Compared with the one-dimensional entropy method and the two-dimensional entropy method, it is obviously superior in noise immunity and detail preservation. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： image segmentation maximum entropy three-dimensional histogram optimization algorithm artificial fish-swarm algorithm

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Multi-Spectral Radiation Temperature Measurement: A High-Precision Method Based on Inversion Using an Enhanced Particle Swarm optimization algorithm with Multiple Strategies

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SENSORS 2024年第18期24卷 6003页

作者： Wang, Xiaodong Han, Shuaifeng Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China

Multi-spectral temperature measurement technology has been found to have extensive applications in engineering practice. Addressing the challenges posed by unknown emissivity in multi-spectral temperature measurement data processing, this paper adds emissivity constraints to the objective function. It proposes a multi-spectral radiation temperature measurement data processing model realized through a particle swarm optimization algorithm improved based on multiple strategies. This paper simulates six material models with distinct emissivity trends. The simulation results indicate that the algorithm calculates an average relative temperature error of less than 0.3%, with an average computation time of merely 0.24 s. When applied to the temperature testing of silicon carbide and tungsten, experimental data further confirmed its accuracy: the absolute temperature error for silicon carbide (tungsten) is less than 4 K (7 K), and the average relative error is below 0.4% (0.3%), while two materials maintain an average computation time of 0.33 s. In summary, the improved particle swarm optimization algorithm demonstrates strong performance and high accuracy in multi-spectral radiation thermometry, making it a feasible solution for addressing multi-spectral temperature measurement challenges in practical engineering applications. Additionally, it can be extended to other multi-spectral systems.

关键词： multi-spectral thermometry temperature inversion spectral emissivity model optimization algorithm data processing

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