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Study of Wireless Sensor Network Route Based on Improved Ant Colony algorithm

INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2016年第10期12卷 86-90页

作者： Bo, Jingyi Wang, Yubin Xu, Na Hebei Normal Univ Sci & Technol Coll Math & Informat Sci & Technol Qinhuangdao 066004 Hebei Peoples R China

Combining the characteristics of wireless sensor network, the ant colony algorithm is applied to a wireless sensor network, and a wireless sensor network route algorithm based on energy equilibrium is proposed in this paper. This algorithm takes the energy factor into the consideration of selection of route based on probability and enhanced calculation of information so as to find out the optimal route from the source node to the target node with low cost and balanced energy, and it prolongs the life cycle of the whole network.

关键词： wireless sensor network ant colony algorithm network route optimization algorithm

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Optimized inversion method for thermal parameters of concrete dam under the insulated condition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期126卷

作者： Bo, Chen Qingyi, Wang Weinan, Chen Hao, Gu Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Water Conservancy & Hydroelect Power Co Ltd Zhejiang Design Inst Hangzhou 310000 Peoples R China

It is crucial to study how thermal characteristics of concrete dam change over time, especially in cold regions, in order to guarantee long-term safety of the engineering projects. In this manuscript, an inverse analysis method by coupling the numerical simulation model and dam safety monitoring data, is proposed to solve the problem that true thermal parameters of the whole structure cannot be obtained by laboratory experiments or field tests of single points. Moreover, a numerical equivalence method of the dam and its insulation layer is introduced, in order to overcome the problem that the thickness of the insulation layer is much smaller than that of the concrete dam, resulting in low accuracy of the simulation model. Thirdly, the particle swarm optimization is introduced, in order to solve the ill-posed problems occurring during the solution process of multi-parameter inverse analysis. Considering the problem that the original particle swarm algorithm is easy to fall into local optimum and stuck edge, this study improves particles velocity and inertia weights. Benchmark function tests are applied to show the performance compared to several typical optimized algorithms. Numerical simulation and engineering verifi-cation are used to demonstrate the rationality and feasibility of the proposed method. The cases show that the residual box diagram of two typical measuring points calculated by inversion value is smaller than the median line and mean point calculated by design value. Moreover, the change law of the temperature field of the finite element calculation using the inversion values is closer to the actual measured value than that of the design value. The results indicate that the multi-parameter inverse method is feasible in insulated dam engineering. In addition, the result of the improved PSO optimization converges faster and has a higher fitness value than that of the original algorithm. It suggests that the improved PSO algorithm for thermal parameters i

关键词： Cold region Concrete dam Dam insulation Equivalent heat release coefficient optimization algorithm

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Rotating Beams Isospectral to Axially Loaded Nonrotating Uniform Beams

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AIAA JOURNAL 2013年第5期51卷 1189-1202页

作者： Kambampati, Sandilya Ganguli, Ranjan Mani, V. Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India

In this paper, the stiffness and mass per unit length distributions of a rotating beam, which is isospectral to a given uniform axially loaded nonrotating beam, are determined analytically. The Barcilon-Gottlieb transformation is extended so that it transforms the governing equation of a rotating beam into the governing equation of a uniform, axially loaded nonrotating beam. Analysis is limited to a certain class of Euler-Bernoulli cantilever beams, where the product between the stiffness and the cube of mass per unit length is a constant. The derived mass and stiffness distributions of the rotating beam are used in a finite element analysis to confirm the frequency equivalence of the given and derived beams. Examples of physically realizable beams that have a rectangular cross section are shown as a practical application of the analysis.

关键词： Finite Element Analysis Cantilever Beam Newton Raphson Method Helicopter Rotor Inverse Problems Mathematical Analysis Material Properties Transverse Vibrations Turbine Blades optimization algorithm

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Dual trace gas detection using a compact two-channel multipass cell with dense and line spot patterns

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HELIYON 2023年第2期9卷

作者： Kong, Rong Ma, Ningyi Liu, Peng Zhou, Xin Beijing Normal Univ Ctr Adv Quantum Studies Dept Phys Appl Opt Beijing Area Major Lab Beijing 100875 Peoples R China

A highly sensitive dual-gas sensor based on a two-channel multipass cell (MPC) was designed and developed for simultaneous detection of atmospheric methane (CH4) and carbon dioxide (CO2) by using two distributed feedback lasers emitting at 1653 nm and 2004 nm. The nondominated sorting genetic algorithm was applied to intelligently optimize the MPC configuration and accelerate the dual-gas sensor design process. A compact and novel two-channel MPC was used to achieve two optical path lengths of 27.6 m and 2.1 m in a small volume of 233 cm3. Simultaneous measurements of CH4 and CO2 in the atmosphere were performed to demonstrate the stability and robustness of the gas sensor. According to the Allan deviation analysis, the optimal detection precision for CH4 and CO2 was 4.4 ppb at an integration time of 76 s and 437.8 ppb at an integration time of 271 s, respectively. The newly developed dual-gas sensor exhibits superior characteristics of high sensitivity and stability, cost-effectiveness and simple structure, which make it well-suited for multiple trace gas sensing in various applications, including environmental monitoring, safety inspections and clinical diagnosis.

关键词： Tunable diode laser absorption spectroscopy Dual -gas sensor Multipass cell optimization algorithm

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UAV formation path planning for mountainous forest terrain utilizing an artificial rabbit optimizer incorporating reinforcement learning and thermal conduction search strategies

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Wang, Wentao Li, Xiaoli Tian, Jun Nankai Univ Coll Software Tianjin 300350 Peoples R China ASTAR Inst Infocomm Res Singapore Singapore

The path planning of Unmanned Aerial Vehicle (UAV) formations plays a crucial role in mountainous forest monitoring missions. However, path planning is particularly challenging due to steep terrain and dense vegetation, making it difficult to generate optimal flight paths. The goal of UAV formation path planning in forest monitoring is to create safe, feasible flight paths for each UAV, avoiding terrain obstacles and ensuring coordination and safety, ultimately improving the quality of mission accomplishment. This study establishes a mathematical model that incorporates multiple constraints, such as flight distance, collision threats, and path stability, effectively transforming the complex problem of UAV formation path planning into an optimization problem. To address this multi-constraint path planning optimization problem, an Artificial Rabbit optimization algorithm incorporating Reinforcement Learning and Thermal conduction search strategy (RLTARO) is proposed. The incorporation of multiple strategies aims to improve the balance of exploration and exploitation of the algorithms as well as algorithmic convergence in the face of complex path planning problems. The comprehensive comparison of the RLTARO algorithm with nine advanced algorithms of similar type in the CEC2017 suite demonstrates its outstanding convergence and robustness across various types of optimization problems. The results of path planning experiments conducted on six mountainous forest terrains with varying complexities demonstrate that RLTARO can efficiently and reliably plan flight paths for UAV formations. Furthermore, the Friedman test results from multiple experiments consistently indicate that RLTARO holds significant performance advantages over the comparison algorithms.

关键词： UAV formation path planning Mountainous forest monitoring Reinforcement learning Thermal conduction search strategies strategy optimization algorithm

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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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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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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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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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