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Enhanced Prairie Dog Optimization with differential evolution for solving engineering design problems and network intrusion detection system

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HELIYON 2024年第17期10卷 e36663页

作者： Alshinwan, Mohammad Khashan, Osama A. Khader, Mohammed Tarawneh, Omar Shdefat, Ahmed Mostafa, Nour Abdelminaam, Diaa Salama Appl Sci Private Univ Fac Informat Technol Amman 11931 Jordan Rabdan Acad Res & Innovat Ctr POB 114646 Abu Dhabi U Arab Emirates Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Middle East Univ MEU Res Unit Amman 11831 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan

This paper introduces a novel hybrid optimization algorithm, PDO-DE, which integrates the Prairie Dog Optimization (PDO) algorithm with the differential evolution (DE) strategy. This research aims to develop an algorithm that efficiently addresses complex optimization problems in engineering design and network intrusion detection systems. Our method enhances the PDO's search capabilities by incorporating the DE's principal mechanisms of mutation and crossover, facilitating improved solution exploration and exploitation. We evaluate the effectiveness of the PDO-DE algorithm through rigorous testing on 23 classical benchmark functions, five engineering design problems, and a network intrusion detection system (NIDS). The results indicate that PDO-DE outperforms several state-of-the-art optimization algorithms regarding convergence speed and accuracy, demonstrating its robustness and adaptability across different problem domains. The PDO-DE algorithm's potential applications extend to engineering challenges and cybersecurity issues, where efficient and reliable solutions are critical;for example, the NIDS results show significant results in detection rate, false alarm, and accuracy with 98.1%, 2.4%, and 96%, respectively. The innovative integration of PDO and DE contributes significantly to stochastic optimization and swarm intelligence, offering a promising new tool for tackling diverse optimization problems. In conclusion, the PDO-DE algorithm represents a significant scientific advancement in hybrid optimization techniques, providing a more effective approach for solving real-world problems that require high precision and optimal resource utilization.

关键词： Prairie dog algorithm differential evolution algorithm Engineering problems Real-world problems Optimization problems

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A new deep recurrent hybrid artificial neural network of gated recurrent units and simple seasonal exponential smoothing

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GRANULAR COMPUTING 2024年第1期9卷 1-10页

作者： Kolemen, Emine Egrioglu, Erol Bas, Eren Turkmen, Mustafa Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkiye Giresun Univ Fac Arts & Sci Dept Biol TR-28200 Giresun Turkiye

Forecasting time series is an important problem in many fields. To overcome this problem, both traditional forecasting methods and artificial neural network methods have been developed. Deep artificial neural networks have become very popular in the time series forecasting literature in recent years. Hybrid structures, in which recurrent neural networks and classical forecasting methods are used together, offer more suitable architectures for solving forecasting problems than other neural networks. Problems arise in the training of simple recurrent neural networks due to the explosion and vanishing gradient problem. In this study, a hybrid artificial neural network architecture consisting of a combination of a gated recurrent unit artificial neural network and a simple exponential smoothing method is proposed for the problem of forecasting seasonal time series. The architecture is inspired by the gated recurrent unit and exponential smoothing model. In addition, training algorithms based on the differential evolution algorithm and particle swarm optimization algorithm, which are widely used in the literature, have been proposed for training the neural network. Both algorithms proposed for training the artificial neural network use different strategies to solve overfitting and local optimum problems. The main contribution of the study is to propose a new hybrid recurrent neural network and its training algorithms for solving the prediction problem of time series containing seasonal components. To determine the performance of the proposed method, it is applied to many seasonal time series and the results are compared with other important artificial neural network models in the literature. As a result of the analysis, it is seen that the proposed deep artificial neural network method could be preferred for solving the seasonal time series forecasting problem.

关键词： Time series Gated recurrent unit artificial neural network differential evolution algorithm Particle swarm optimization Forecasting

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Multiobjective differential evolution algorithm Using Binary Encoded Data in Selecting Views for Materializing in Data Warehouse

Multiobjective Differential Evolution Algorithm Using Binary...

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4th International Conference on Swarm, evolutionary, and Memetic Computing (SEMCCO)

作者： Goswami, Rajib Bhattacharyya, Dhruba Kumar Dutta, Malayananda Tezpur Univ Dept Comp Sci & Engn Tezpur 784028 India

ISBN: (纸本)9783319037554;9783319037561

In this paper, we define the view selection process for materializing in data warehouse as a multiobjective optimization problem. We have implemented multiobjective differential evolution (DE) algorithm for binary encoded data to solve this problem. In our approach, to control population in intermediate generations of the differential evolution process by maintaining diversity in solution space with necessary elitism, the solutions of intermediate generations are first ranked according to their pareto dominance levels and then the diversity among solution vectors in solution space is measured. The algorithm is found to be suitable in selecting significant representitive solutions from a large number of nondominating solutions of the view selection problem.

关键词： Data warehouse View materialization differential evolution algorithm Multiobjective optimization

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Finite-time control strategy for swarm planar underactuated robots via motion planning and intelligent algorithm

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MEASUREMENT & CONTROL 2023年第3-4期56卷 813-819页

作者： Huang, Zixin Wei, Shaoqi Hou, Mengyu Wang, Lejun Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China Wuhan Text Univ Hubei Key Lab Digital Text Equipment Wuhan Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Complex Syst & Bion Control Chongqing 400065 Peoples R China

The swarm planar underactuated robots have the ability to organize each robot to complete task in a finite time and the characteristics of ignoring gravity, energy saving, light weight, and so on. In this paper, we propose control strategy for such robot via motion planning and intelligent algorithm. First, we establish a unified dynamic model and analysis its underactuated characteristics. In order to enable all links to reach the target position smoothly in a finite time, a suitable trajectory is planned and parameters are optimized by the differential evolution algorithm (DEA). Then, the controllers are designed for each planar underactuated robot. Finally, the simulation results show that the proposed strategy is effective.

关键词： Finite-time control swarm planar underactuated robots motion planning differential evolution algorithm

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Short-Term Load Forecasting Based on Multi-Scale Ensemble Deep Learning Neural Network

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IEEE ACCESS 2023年 11卷 111963-111975页

作者： Shen, Qin Mo, Li Liu, Guanjun Zhou, Jianzhong Zhang, Yongchuan Ren, Pinan Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Prov Key Lab Digital Watershed Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Water Resources & Hydropower Wuhan 430074 Peoples R China

High-precision load forecasting is crucial for the power system planning and electricity market transactions. Recently, deep learning models have been widely used due to their powerful data mining capabilities. However, the existing research mainly focus on model structure adjustment and input feature selection, ignoring the influence of model ensemble on prediction. A single deep learning model is not yet able to address the various complex challenges that arise in short-term load forecasting. To improve the accuracy of short-term load forecasting, this paper proposes a novel multi-scale ensemble method and multi-scale ensemble neural network. This neural network uses long short-term memory, gate recurrent units, and temporal convolutional network as the basic model. By coupling the stochastic weight averaging ensemble method and differential evolution ensemble method, these deep learning networks were assembled from single-model scale and multi-model scale, respectively, thereby effectively improving the model prediction accuracy. For predicting the power load of Hubei Province in China, meteorological features and time features were in consideration. The proposed model was trained and compared with eleven intelligent short-term load forecasting models, including machine learning, deep learning and ensemble deep learning models. Simulations show that the proposed model has the best comprehensive prediction performance. This study highlights the power of ensemble deep learning models coupled with multiple ensemble techniques and the promising prospect of our proposed model in short-term load forecasting.

关键词： Short-term load forecasting ensemble model stochastic weight averaging deep learning differential evolution algorithm

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Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems

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APPLIED SOFT COMPUTING 2023年 132卷

作者： Rauf, Hafiz Tayyab Gao, Jiechao Almadhor, Ahmad Haider, Ali Zhang, Yu-Dong Al-Turjman, Fadi Staffordshire Univ Ctr Smart Syst AI & Cybersecur Stoke On Trent Staffs England Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Sakaka 72388 Saudi Arabia Univ Gujrat Dept Comp Sci Gujrat Pakistan Univ Leicester Sch Informat Leicester LE1 7RH Leics England Univ Kyrenia Fac Engn Res Ctr AI & IoT TR-10 Mersin Turkiye Near East Univ AI & Robot Inst Artificial Intelligence Engn Dept Mersin 10 Nicosia Turkiye

differential evolution (DE) is a simple but powerful evolutionary algorithm used in multiple sciences and engineering disciplines to tackle optimization problems. DE has some disadvantages, such as premature convergence and the low convergence rate that prompts the worst DE execution structure in the constrained environment. The occurrence of these constraints split up the exploration area into viable and un-viable intervals. To overcome the abovementioned issues, we chose to take advantage of the vital characteristics of two mutation strategies: DE/rand/1 and DE/best/2. This research proposes a novel DE variant called Multi-population-based chaotic DE (MPC-DE) to solve multi-model and multi-objective optimization problems. The proposed MPC-DE is divided into two sub-populations with chaotic-based enhanced population initialization approaches, Sinusoidal and Tent map chaotic population initialization. Each sub-population follows the proposed improved mutation strategies based on two-dimensional chaotic maps, i.e., Baker's map and Arnold's Cat Map for DE/rand/1 in the first sub-population, and Zaslavskii Map for DE/best/2 in the second sub-population. Finally, the selection criteria are proposed to select the best offspring produced by each sub-population following the mutant vectors generated by the proposed mutation strategies. MPC-DE is evaluated on the dynamic multi-model and multi-objective optimization problems, i.e., benchmark problems for CEC 2017 and CEC 2020, respectively. To verify MPC-DE's performance, we compare it with the latest DE variants, namely, EFADE, MPEDE, SHADE, EPSDE, L-SHADE, ESMDE, CoDE, and JADE. The proposed MPC-DE is also employed to solve the Economic Load Dispatch Problem (EDP) and reduce fuel costs. We used a 60-unit bus system and a 180-unit bus system to solve EDP and compared it to recent EDP solvers such as DPADE, JADE, EPSDE, SaDE, DE/BBO, DE, MIMO, TLBO, BPSO, CSO, ORCSA, CSA, ORCCRO, BBO, and ED-DE. The empirical results confirm

关键词： differential evolution algorithm Multi -modal optimization Multi -objective optimization Economic load dispatch problem Baker?s map Arnold?s cat map Zaslavskii map

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Application Of AFFDE-RBF-PID-PI Cascade Control In Heating Furnace Temperature Control System

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第7期27卷 2767-2775页

作者： Liu, Yue-Ting Zhang, Yan Lanzhou Univ Arts & Sci Sch Media Engn Lanzhou 730000 Peoples R China

Given the large hysteresis in the material outlet temperature of the heating furnace, a differential evolution algorithm with adaptive adjustment factors (AAFDE)-radial basis function (RBF)-proportional integral derivative (PID)-PI cascade is proposed. First, we introduce an adaptive mutation factor into the differential evolution (DE) algorithm and define the individual merit coefficient to incorporate a self-adaptive crossover probability factor. Second, the initial parameters of RBF are optimized by the AFFDE algorithm, and RBF neural network model is established. Then, we obtain gradient information by RBF online identification. Finally, we perform online adjustments to the three parameters of PID based on the gradient information. The three parameters are applied to the adjustment of the main controller, while the sub-controller employs PI control. Experimental results show that the anti-disturbance performance of the AFFDE-RBF-PID-PI cascade control outperforms the improved differential evolution (IDE)-RBF-PID-PI cascade control, generalized opposition-based differential evolution (GODE)-RBF-PID-PI cascade control, and MCOBDE-RBF-PID-PI cascade control respectively by 16%, 11%, and 7%, with the response speed improving by 18%, 12%, and 9%, and the stability improving by 19%, 13%, and 8%. These results show that AFFDE-RBF-PID-PI cascade control exhibits enhanced anti-disturbance performance, faster response speed, improved stability, and superior control effect.

关键词： adaptive adjustment factor differential evolution algorithm radial basis function neural network cascade control heating furnace system

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A two-step evolutionary algorithm for the distributor's pallet loading problem with multi-size pallets

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2023年第4期35卷 1256-1275页

作者： Mungwattana, Anan Piyachayawat, Thanakrit Janssens, Gerrit K. Kasetsart Univ Dept Ind Engn Fac Engn 50 Paholyothin Rd Jatujak 10900 Bkk Thailand Hasselt Univ Logist Res Grp Fac Business Econ Agoralaan 1 B-3590 Diepenbeek Belgium

A two-step evolutionary algorithm for solving a real-life Distributor's Pallet Loading Problem (DPLP) is presented. Previous literature considers multiple sizes of boxes to be loaded on the same size pallet for the DPLP. However, in this research, multiple-size pallets are allowed. Allowing multi-size pallets is new to the problem and makes it even harder to solve, because the DPLP with the same size pallet is NP-Hard already. Because of the complexity and size of the problem, a two-step algorithm, based on an evolutionary algorithm and a differential evolution algorithm, is developed to solve the problem. The first step is to determine the number of pallets of each type, and to assign boxes onto each pallet. The second step is used for improving the solutions obtained from the first step. The effectiveness of the algorithm is evaluated by using a benchmark problem set based on 3D bin-packing problem, and real-life cases from a factory. For the benchmark problem set, the total number of pallets used, as suggested by the newly developed algorithm, is 0.42% higher than the worst results of the exact algorithm on average. However, the results, regarding computation time show that the proposed algorithm is able to find sufficiently good solutions with 78.54% less average calculation time of all problems which the algorithm performed better. As for the real-life cases, the solutions obtained by the algorithm are better both in terms of calculation time and the number of pallets required, which are 26.76% and 28.02% less of the average total occupied cross-section area, and 7 and 29 min of the average total computation time with alpha = 0.7 and alpha = 0.8, respectively.

关键词： Distributor's pallet loading problem Multi-size pallet evolutionary algorithims differential evolution algorithm

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An Improved PI Controller for Stiction Compensation of Control Valves in Process Industry 43

An Improved PI Controller for Stiction Compensation of Contr...

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43rd Chinese Control Conference (CCC)

作者： Yang, Dong Lang, Xun Wu, Jiande Lang, Yumin Xie, Lei Su, Hongye Yunnan Univ Dept Elect Engn Sch Informat Kunming 650091 Yunnan Peoples R China Yunnan Univ Yunnan Key Lab Intelligent Syst & Comp Kunming 650500 Yunnan Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In the process industry, limit cycles are mostly induced by stiction-related nonlinearity in control valves, severely deteriorating control loop performance. Such problem highlights the importance of compensating for oscillations due to valve stiction. To this end, this work introduces an improved PI controller (IPIC) for stiction compensation, which retains the advantages of the traditional PI controller with fewer adjustment parameters. The enhancement involves nonlinear variation of the integral gain based on instantaneous error. Additionally, a differential evolution algorithm is utilized for controller tuning. Compared to state-of-the-art methods in setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, the proposed approach exhibits superior performance.

关键词： Stiction Limit Cycle Nonlinear PI controller differential evolution algorithm

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Using Innovative differential evolution algorithm for OFDM Reducing PAPR

Using Innovative Differential Evolution Algorithm for OFDM R...

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7th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Lee, Shu-Hong Hung, Ho-Lung Chien Kuo Technol Univ Dept Electron Engn 1 Chieh Shou N Rd Changhua 500 Taiwan Chien Kuo Technol Univ Dept Elect Engn Changhua 500 Taiwan

ISBN: (纸本)9780769549743

In this paper, tone injection (TI) based on differential evolution algorithm (DE) is proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. TI is a distortionless PAPR reduction technique, but its high search complexity for finding optimal TI scheme requires an exhaustive search over all combinations of possible permutations of the expanded constellation, which is a potential problem for practical applications. In this work we present a novel convex optimization approach to numerically determine the near-optimal tone injection (TI) solution based on a differential evolution algorithm (DETI). DETI is compared to different TI schemes for PAPR reduction and search complexity performances. The simulation results show that the proposed DETI method provides good PAPR reduction and bit error-rate (BER) performances. Finally, the DETI algorithm not only reduces the PAPR significantly, but also decreases the computational complexity. The simulation results show that it achieves more or less the same PAPR reduction as that of exhaustive search

关键词： Orthogonal frequency division multiplexing Peak-to-average power ratio reduction Tone injection differential evolution algorithm

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