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A novel Fruit Fly Optimization algorithm with quasi-affine transformation evolutionary for numerical optimization and application

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2022年第2期18卷

作者： Wang, Ru-Yu Hu, Pei Hu, Chia-Cheng Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn 579 Qianwangang RdXinan St Qingdao 266590 Shandong Peoples R China Nanyang Inst Technol Sch Software Nanyang Peoples R China Yango Univ Coll Artificial Intelligence Fuzhou Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

The Fruit Fly Optimization algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, Fruit Fly Optimization algorithm tends to converge prematurely. In response to this situation, this article proposes a new optimized structure-Quasi-affine Transformation evolutionary for the Fruit fly Optimization algorithm. The new algorithm uses the evolution matrix in QUasi-Affine TRansformation Evolution algorithm to update the position coordinates of particles. This strategy makes the movement of particles more scientific and the search space broader. In order to prove its effectiveness, we compare Quasi-affine Transformation evolutionary for the Fruit fly Optimization algorithm with five other mature intelligent algorithms, and test them on 22 different types of benchmark functions. In order to observe the multi-faceted performance of Quasi-affine Transformation evolutionary for the Fruit fly Optimization algorithm more intuitively, we also conduct experiments on algorithm convergence analysis, the Friedman test, the Wilcoxon signed-rank test, and running time comparison. Through the above several comparative experiments, Quasi-affine Transformation evolutionary for the Fruit fly Optimization algorithm has indeed demonstrated its strong competitiveness. Finally, we apply it to Capacitated Vehicle Routing Problem. Through comparing with the contrast algorithms, it is confirmed that Quasi-affine Transformation evolutionary for the Fruit fly Optimization algorithm can achieve better vehicle routes planning.

关键词： intelligence algorithm Fruit Fly Optimization algorithm evolution matrix Quasi-affine Transformation Evolution algorithm Capacitated Vehicle Routing Problem

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Reactive power optimization in distribution network with PV station based on intelligence algorithm

Reactive power optimization in distribution network with PV ...

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3rd International Conference on Advanced Engineering Materials and Technology (AEMT 2013)

作者： Ran, Xinwei Xu, Jianzheng Shandong Univ Sch Elect Engn Jinan 250100 Peoples R China

ISBN: (纸本)9783037857649

Reactive power problem of PV station in distribution power system is discussed. Probability theory is introduced to calculate the expectation of active power, which is approximately used to replace the randomly changing output. Reactive output can be adjusted by changing some related parameters of the grid-connected PV system. Considering reactive power of PV station as control variables, a model with voltage level constraints of minimizing the active power loss is established and its optimal solution is figured out with IDCC (Improved Bacterial Colony Chemotaxis). Case calculation results show the validity of above-mentioned model and algorithm.

关键词： distributed generation PV station reactive power optimization intelligence algorithm active power loss

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Data skyline query protocol based on parallel genetic improvement decision tree

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JOURNAL OF SUPERCOMPUTING 2020年第2期76卷 1116-1127页

作者： Zeng, Yifu Zhou, Yantao Zheng, Fei Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Query optimization of database requires high speed and high efficiency. In order to solve the low efficiency problem and difficulty in obtaining optimal solution existing in the current query optimization algorithm of database, a query optimization of database based on multi-group firefly algorithm (MGFA) is proposed, combining with characteristics of database query and advantage of firefly algorithm. Firstly, the firefly group is divided into multiple groups with different parameters, and each group of fireflies followed the optimal firefly in the same group for optimizing. Then, mutual learning mechanism is established among various groups of optimal fireflies to realize inter-group information exchange. At last, query optimization data of database are adopted for the simulation experiment. Experiment results indicate that MGFA is a query optimization method of database with good performance. It can obtain better query result than other algorithms do.

关键词： Database query intelligence algorithm Decision tree Skyline query Query optimization

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Intelligent evaluation of the interior sound quality of electric vehicles

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APPLIED ACOUSTICS 2021年 173卷

作者： Qian, Kun Hou, Zhichao Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

In this paper, an intelligent evaluation of the interior sound quality (SQ) of electric vehicles (EVs) was designed using intelligent algorithms and artificial neural networks. First, interior noises from different EV brands and models were collected using noise tests that are based on relevant standards. Second, a subjective evaluation was applied to appraise the SQ using a laboratory-scale jury test. Meanwhile, the objective evaluation of the SQ was quantitatively analyzed using eight acoustic characteristic parameters: A-weighted sound pressure level, loudness, sharpness, roughness, fluctuation strength, articulation index, tonality, and impulsiveness. Third, simulated annealing (SA) and genetic algorithm (GA) were used to optimize the backpropagation neural network (BPNN). Based on the subjective and objective evaluation results of the SQ of the tested EVs, an intelligent evaluation model of the interior SQ of EVs was designed using SAGA-BPNN. Based on the weight analysis of SAGA-BPNN, the key objective parameters that had a great impact weight were identified. Also, the prediction error of the reconstructed model, which was built by the key objective parameters, was very small and basically consistent with the original model. This evaluation model can not only accurately evaluate the SQ of EV, but it can also serve as a convenient and effective evaluation tool for the acoustic design of EVs. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Electric vehicle (EV) Sound quality (SQ) Noise intelligence algorithm Artificial neural network (ANN)

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Research on Denoising Method for Improving the Identification Accuracy of Structural Damage

Research on Denoising Method for Improving the Identificatio...

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作者： Chao Ping Luo De Qing Guan Sai He Fan Gao School of Civil Engineering Changsha University of Science and Technology

Under the influence of equipment and environment, gaussian white noise will be accompanied in the process of obtaining the measured vibration mode of the structure, thus masking the effective features of signal and disturbing the effect of damage identification. To avoid this problem, this paper presented a new denoising method. Firstly, the multi-index evaluation function constructed by analytic hierarchy process is used to select the optimal wavelet base and the decomposition layers' s number. Secondly, we use the newly constructed wavelet threshold function to research the wavelet coefficients in the high frequency part of the signal to remove the noise coefficients. Simulation results show: the new denoising method is contributing in improving the SNR of vibration signals and reducing the RMS error. The experimental results testified that the new method can effectively increase the accuracy of structural damage identification.

关键词： Vibration signal damage identification wavelet denoising analytic hierarchy process intelligence algorithm

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Artificial Bee Colony algorithm and Its Application in Traveling Salesman Problems 31

Artificial Bee Colony Algorithm and Its Application in Trave...

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31st Chinese Control And Decision Conference (CCDC)

作者： Li Xutong Zheng Yan Northeastern Univ Sch Informat Sci Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728101057

The purpose of applying the artificial bee colony algorithm to the traveling salesman problems pursuant to its principle is to find out solutions to the traveling salesman problems of different cities and use local search strategy to effectively make such issues less complicated, so as to optimize the path. It is necessary to analyze the correlation between the artificial bee colony algorithm and relevant algorithm parameters and the number of cities. Simulate result denotes the artificial bee colony algorithm can be applied to settle the problems of traveling salesman and is much reliable in effectiveness, usability and researchability.

关键词： Swarm intelligence intelligence algorithm Artificial bee colony algorithm Traveling salesman problems

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CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests

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BMC BIOINFORMATICS 2017年第1期18卷 1-18页

作者： Ma, Li Fan, Suohai Jinan Univ Sch Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China

Background: The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. Results: We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. Conclusion: The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

关键词： Random forests Imbalance data intelligence algorithm Feature selection Parameter optimization

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A Stochastic Bilevel Programming Model for the Iron and Steel Production Optimization Problem Under Carbon Trading Mechanism 10th

A Stochastic Bilevel Programming Model for the Iron and Stee...

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10th International Conference on Management Science and Engineering Management (ICMSEM)

作者： Tao, Zhimiao Sichuan Univ Low Carbon Technol & Econ Res Ctr Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9789811018374;9789811018367

In this paper, we focus on the optimal production problem for iron and steel problem under the carbon emission trading mechanism. A bilvel programming model with stochastic factor is developed, in which the leader and follower are the authority and the plant, respectively. Some results are derived from a case study. Finally, policy suggestions are proposed to the help improve the decision-making process of the plant and authority simultaneously.

关键词： Stochastic bilevel programming model Iron and steel production intelligence algorithm Carbon trading mechanism

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The effect of rescheduling power plants and optimal allocation of STATCOM in order to Improve power system static security Using TLBO algorithm 25

The effect of rescheduling power plants and optimal allocati...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Haggi, Hamed Marjani, Saeed Rezaeian Golkar, Masoud Aliakbar KN Toosi Univ Technol Elect Engn Tehran Iran Urmia Univ Elect Engn Orumiyeh Iran

ISBN: (纸本)9781509059638

Contingency occurrence in power systems and studying system conditions in these situations accordingly is an avoidable issue. One of the important practical measurements in power systems to preserve power system static security situation in case of contingencies is rescheduling generator's power output. This rescheduling is performed so that the generators have the least deviation from their optimal power output. On the other hand, FACTS devices also have great effect on increasing power system security. Therefore, the most important facing challenge in rescheduling generators' power output, and simultaneously, using FACTS devices is to determine the optimal rate of changes in generators' power output and also determine the best place and appropriate size of FACTS devices in order to preserve static security of power system. In this paper, the simultaneous effect of rescheduling the power output of power plants and optimal allocation of static compensator (STATCOM) is considered as the most effective type of FACTS devices for increasing static security of power system with regard to increasing the intensity of lines overload and the intensity of voltage deviation (VD) above the allowed limit of load buses in case of probable events. The considered contingency is transmission line outage from network. In order to determine the unknown variables including changes of generator's output and also appropriate size and place of static compensator STATCOM, and in order to optimize defined objective function, teaching-learning based algorithms (TLBO) were used. Also, in order to investigate efficiency of this innovative algorithm in presenting high quality solutions, IEEE 30-bus test network was used.

关键词： Rescheduling STATCOM Power System Security Losses Overload Voltage Deviation intelligence algorithm TLBO

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Multi-objective particle swarm optimization algorithm based on leader combination of decomposition and dominance

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第3期33卷 1577-1588页

作者： Hu, Ziyu Yang, Jingming Cui, Huihui Sun, Hao Wei, Lixin Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

Convergence and well-distribution are two basic issues in multi-objective optimization problems(MOPs). However, it is hard to optimize them simultaneously for the selection of leader particle is not always leading the population to the Pareto front. To make a better performance of multi-objective particle swarm optimization algorithm(MOPSO), decomposition and domination leadership particle selection mechanism have been introduced into MOPSO. Decomposition leader particle selection mechanism is used to keep the swarm with diversity, while domination leader particle selection mechanism make the particles move to the Pareto front. The performance of our proposed method is validated based inverted generation distance(IGD) and compared with five state-of-the-art algorithms on a number of unconstrained benchmark problems. Empirical analysis demonstrates the superiority of our proposed method on both proximity and diversity.

关键词： intelligence algorithm multi-objective optimization particle swarm optimization soft computing

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