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Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Ustun, Deniz Toktas, Abdurrahim Erkan, Ugur Akdagli, Ali Engn Fac Dept Comp Engn TR-33400 Tarsus Mersin Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Elect Elect Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Comp Engn TR-70200 Karaman Turkey Mersin Univ Engn Fac Dept Elect Elect Engn TR-33343 Mersin Turkey

Artificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee;in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted "modified ABC by DE" (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergence.

关键词： Artificial bee colony (ABC) Differential evolution (DE) optimization algorithm Modified ABC Modified algorithm Hybrid algorithm

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Reply by Authors to Comment on "Roll Control Using Only Synthetic Jet Actuators at High Angle of Attack"

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JOURNAL OF AIRCRAFT 2017年第3期54卷 1240-1240页

作者： Li, Chunzhi Yang, Jianying Peking Univ State Key Lab Turbulence & Complex Syst Dept Mech & Engn Sci Coll Engn Beijing 100871 Peoples R China

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Optimal design of facility allocation and maintenance strategy for a cellular network

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 205卷 107253-107253页

作者： Wu, Shengna Yang, Jun Peng, Rui Zhai, Qingqing China Inst Atom Energy Dept Isotope Beijing Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Beijing Univ Technol Sch Econ & Management Beijing Peoples R China Shanghai Univ Sch Management Shanghai Peoples R China

A cellular network is widely used for transmitting text (i.e., alphanumeric) messages between subscribers from different geographical locations. The geographic area of a cellular network is divided into a large number of cells, which are served by at least one facilities from base stations among predetermined positions. Both reliability and cost of a cellular network depend on the number, location and failure rate of facilities in the base stations. Thus, the joint optimization of facility allocation and maintenance strategy in a cellular network is relevant and significant for guiding optimal decisions on cellular network planning. This paper models a cellular network with repairable facilities characterized by a two-parameter Weibull *** objective is to jointly optimize the facility allocation among predetermined positions and the facilities maintenance strategy in order to minimize the expected life cycle cost of the cellular network. An optimization algorithm for the proposed model is given based on heuristic algorithm and simulation. A case study is presented to illustrate the application of the proposed model, and sensitivity analysis shows the relationship between the optimal preventive replacement interval and the ratio of the preventive replacement cost to the minimal repair cost.

关键词： Cellular network Performance reliability Life cycle cost optimization algorithm Sensitivity analysis

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Short-term industrial load forecasting based on error correction and hybrid ensemble learning

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ENERGY AND BUILDINGS 2024年 313卷

作者： Fan, Chaodong Nie, Shanghao Xiao, Leyi Yi, Lingzhi Li, Gongrong Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the power system. Due to the strong fluctuation and complex characteristics of industrial loads, it is difficult to accurately predict shortterm power demand. To address this issue, this paper proposes a deep learning prediction model based on hybrid ensemble and error correction. The proposed model is divided into two phases: in the first phase, deep power features are extracted from multivariate data through a hybrid ensemble strategy consisting of Random Subspace, Boosting, Ensemble Pruning, and Multi-Objective Molecular Dynamics Theory optimization algorithm (MMDTOA). First, the strategy splits high-dimensional industrial data into multiple sub-datasets. Subsequently, for the features of each sub-dataset, the proposed MMDTOA is applied to perturb the parameters of GRU to generate base learning machines that balance accuracy and diversity. Finally, these base learning machines are integrated by kernel ridge regression stacking. Among them, the two-stage selection strategy and co-evolutionary strategy are embedded into the MMDTOA to enhance the optimization searching effect;in the second stage, a combined error correction strategy is proposed by utilizing the residual information in the prediction results. By combining the dynamic Gaussian error correction function and GRU error correction model, the prediction accuracy of the ensemble model is further improved;Experimental results on real Korean datasets show that the proposed method achieves a minimum value of 3.684% and 7.266% in NMAE and NRMSE, which outperforms eight comparative models, such as SVM, ELM, and CNN, with higher accuracy and robustness.

关键词： Industrial Load Multi -objective Molecular Dynamics Theory optimization algorithm Ensemble Learning Error Correction

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A hybrid day-ahead electricity price forecasting framework based on time series

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ENERGY 2023年 264卷

作者： Xiong, Xiaoping Qing, Guohua Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Zhuang Peoples R China

Electricity price forecasting (EPF) plays an indispensable role in the decision-making processes of electricity market participants. However, the complexity of electricity markets has made EPF increasingly difficult. Currently, popular methods for EPF are based on signal decomposition and suffer from computational redundancy and hyperparameter optimization challenges. In this paper, we propose a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies. First, an adaptive copula-based feature selection (ACBFS) algorithm based on the maximum correlation minimum redundancy criterion is proposed for selecting model input features. Second, a new method of signal decomposition technique for EPF field is proposed based on decomposition denoising strategy. Third, a Bayesian optimization and hyperband (BOHB) optimized long short-term memory (LSTM) model is used to improve the effect of hyperparameter settings on the prediction results. The effectiveness of the different techniques was broadly cross-validated using five datasets set up for the PJM electricity market, and the results indicated that the proposed hybrid algorithm is more effective and practical for day-ahead EPF.

关键词： Electricity price forecasting Copula entropy Feature selection Variational mode decomposition optimization algorithm Long short-term memory

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An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning

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SENSORS 2021年第5期21卷 1814-1814页

作者： Tang, An-Di Han, Tong Zhou, Huan Xie, Lei Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China

The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Levy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.

关键词： unmanned aerial vehicle optimization algorithm equilibrium optimizer constrained optimization path planning

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Deep adaptive feature enrichment

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 162卷 113780-113780页

作者： Taghipour-Gorjikolaie, Mehran Sadri, Javad Razavi, Seyed Mohammad Univ Birjand Fac Elect Engn Dept Elect Engn Birjand Iran Concordia Univ Fac Engn & Comp Sci Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

Features play an important role in the performance of machine learning and classification applications. Usually, separability of classes by using raw or original features are so low, and it is necessary to use complex classifiers with high computational costs or use enrichment modules to increase distinctiveness of features. In this paper, a deep feature enrichment method is proposed to increase the distinguishing power of features using an adaptive neural network-based structure. Proposed method adaptively uses linear/non-linear activation functions for coding, and the dimension of the coding space adaptively adjusted to be lower, the same, or higher than the original feature space. Then the best neural network structure (number of layers and neurons per layers) and the optimum weights for the proposed neural structure are optimized using an evolutionary optimization algorithm. Optimized modules can map/code raw input features into an enriched feature space that can increase the separability of the data points among classes. In fact, our obtained enriched features can adapt themselves to the nature of the training data and they can improve the generalization power also the performance of conventional classifiers. Experimental results on popular UCI datasets such as Glass, Liver, Iris, Wine, Breast cancer and seeds show increase of significant correct recognition rates (11.63% for Glass, 4.35% for Liver, 13.34% for Iris, 27.78% for Wine, 0.72% for Breast cancer and 11.9% for seeds) and also improvement of more than 1.5% of verification rate and 2% of Identification rate for the Yale face database. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Deep learning Deep features Feature enrichment Neural network optimization algorithm Face verification

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Updating assembly parameters for spacecraft assembly process state changes: based on data fusion method

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FRONTIERS IN PHYSICS 2024年 12卷

作者： Liu, Yue Li, Lijuan Lin, Xuezhu Guo, Lili Sun, Jing Wang, Hao Changchun Univ Sci & Technol Sch Optoelect Engn Changchun Peoples R China Changchun Univ Sci & Technol Zhongshan Inst Zhongshan Peoples R China

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.

关键词： thermal protection system fiber optic sensor data 3D point cloud data data fusion state change assembly parameters model establishment optimization algorithm

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The Application of Accurate Calculation of Magnetic Field Intensity in 1.5-T Superconducting MRI Magnet Design

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2012年第6期22卷 4402206页

作者： Feng, Zhongkui Li, Lankai Gao, Chunjie Zhu, Guang Li, Yi Li, Xian Dai, Yinming Wang, Qiuliang Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China

Currently, when calculating the magnetic field generated by the solenoid coil of the superconducting wire wound, we assume that the coil cross section with a uniform current density, but actual current in superconducting wires (NbTi) in the form of a wire in channel is not evenly distributed, the current distribution only in the superconducting core, i.e., there is no current in copper, insulation, and filler, and this method of calculation will result in errors. In this paper, we model the superconducting cores of the 1.5-T superconducting magnetic resonance imaging (MRI) magnet to calculate accurate magnetic field intensity and inhomogeneity by helicoidal method in the diameter of spherical volume and find that inhomogeneity is eight times bigger than that calculated by spherical harmonic expansions, which cannot be accepted in design. Hence, in order to design a high-homogeneity MRI magnet, we amend the 1.5-T MRI magnet's original parameters by an optimization algorithm through an original interface between OPERA-3D and MATLAB according to the accurate results.

关键词： Accurate calculation helicoidal method (HM) inhomogeneity optimization algorithm spherical harmonic expansions (SHE)

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Intra City Transport On-demand

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IFAC-PapersOnLine 2022年第11期55卷 167-172页

作者： Nikolay Netov Kamen Spassov Sofia University “St. Kliment Ohridski” Sofia 1113 Bulgaria

An algorithm to calculate optimal trajectories of e-buses to fulfil on-demand requests for transportation in a smart city environment is presented. Instead of solving the transportation problem once when all transportation requests are registered, we solve the transportation problem at any virtual or public station during the taxi. This approach enables gathering transportation requests dynamically and also consider changes in the environment (e.g. weather conditions, roadblocks, traffic jams, etc) during the transportation. The algorithm is tested with real data. Some results of the simulation are presented.

关键词： Transport on demand Optimal route Transportation problem optimization algorithm Model Simulation Green city Smart city Optimal control

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