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A hyperchaotic map with distance-increasing pairs of coexisting attractors and its application in the pelican optimization algorithm

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CHAOS SOLITONS & FRACTALS 2023年第1期173卷

作者： Ge, Xizhai Li, Chunbiao Li, Yongxin Yi, Chenlong Fu, Haiyan Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Qilu Inst Technol Collaborat Innovat Ctr Memrist Comp Applicat CICMC Jinan 250200 Peoples R China

When both an absolute value function and a cosine function are introduced for chaotic sequence generation, coexisting attractors with different polarities and locations could be extracted by the offset boosting of the initial condition, and thus a hyperchaotic map with distance-increasing coexisting attractors is constructed. It is found that this map has two controllers for rescaling the oscillation totally and partially. The digital circuit implementation shows good consistency with numerical simulation. Moreover, the parameter adaptation in the basic pelican optimization algorithm (POA) is improved by the above-mentioned hyperchaotic map. Correspondingly, the Hyperchaotic pelican optimization algorithm (HCPOA) is constructed, where the hyperchaotic sequences are employed as the sampling pool when random numbers are required in the basic POA. Furthermore, the impact of chaos regulation on the performance of HCPOA is investigated, and the indicators of HCPOA are analyzed by employing five benchmark functions, where the single attractor shows better performance than the double-cavity attractor. The methods of HCPOA proposed in this paper bring the chance to improve the quality of solutions and the ability to escape local optima.

关键词： Hyperchaotic map Coexisting attractors Amplitude control pelican optimization algorithm Benchmark function

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Short-Term Load Forecasting Based on pelican optimization algorithm and Dropout Long Short-Term Memories-Fully Convolutional Neural Network optimization

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ENERGIES 2024年第23期17卷 6115页

作者： Wang, Haonan Huang, Shan Yin, Yue Gu, Tingyun Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Elect Power Res Inst Guizhou Power Grid Guiyang 550007 Peoples R China

In order to improve the prediction accuracy of short-term power loads in a power system, this paper proposes a short-term load prediction method (POA-DLSTMs-FCN) based on a combination of multi-layer lost long short-term memory (DLSTM) neural networks, fully convolutional neural networks (FCNs) and the pelican optimization algorithm (POA). This method firstly uses DLSTMs to extract the time-series features of the load data, which can effectively capture the dynamic changes in the time series;subsequently, it combines the convolution operation of FCNs to obtain high-resolution information between the load data and the features, which enhances the expressive ability of the model. Through a parallel structure, DLSTMs and FCNs can jointly optimize the information extraction and then construct a more accurate load forecasting model. In addition, the learning rate, the number of hidden neurons and the deactivation probability of the Dropout layer in DLSTMs are optimized by the POA to further enhance the performance of the model. The experimental results show that the proposed optimization method has significant advantages over traditional DLSTMs and FCN-LSTM models in terms of prediction accuracy and stability.

关键词： short-term load forecasting pelican optimization algorithm LSTM FCN

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Enhancement Method Based on Multi-Strategy Improved pelican optimization algorithm and Application to Low-Illumination Forest Canopy Images

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FORESTS 2024年第10期15卷 1783页

作者： Zhao, Xiaohan Zhu, Liangkuan Wang, Jingyu Mohamed, Alaa M. E. Northeast Forestry Univ Coll Comp & Control Engn Harbin 150040 Peoples R China Suihua Univ Sch Elect Engn Suihua 152000 Peoples R China

Enhancement is a crucial step in the field of image processing, as it significantly improves image analysis and understanding. One of the most commonly used methods for image contrast enhancement is the incomplete beta function (IBF). However, the key challenge lies in determining the optimal parameters for the IBF. This paper introduces a multi-strategy improved pelican optimization algorithm (MIPOA) to address the low-illumination color image enhancement problem. The MIPOA algorithm utilizes a nonlinear decreasing coefficient to boost the exploration ability and convergence speed, whereas the Hardy-Weinberg principle compensates for the unsound exploitation mechanism. Additionally, the diversity variation operation improves the ability of the algorithm to escape local optimal solutions. The performance of the proposed MIPOA algorithm was evaluated using a benchmark function and was found to outperform five variant algorithms in extensive comparisons. To further harness the potential of the MIPOA algorithm, the authors propose a low-light forest canopy image enhancement method based on the MIPOA algorithm. The MIPOA algorithm searches for the optimal parameters of the IBF, leading to fast contrast enhancement of the image. The segmented gamma correction function is designed to enhance the brightness of the low-light forest canopy images. In determining the optimal parameters of IBF, the MIPOA algorithm demonstrates superior performance compared to other intelligent algorithms in the feature similarity index (FSIM), entropy, and contrast improvement index (CII) of 75%, 58.33%, and 75%, respectively. The proposed MIPOA-based enhancement method achieves a moderate pixel mean and surpasses the conventional enhancement method with an average gradient of 91.67%. The experimental results indicate that the MIPOA effectively addresses the limitations of low optimization accuracy in IBF parameters, and the enhancement method based on the MIPOA provides a more efficacious app

关键词： low-illumination forest canopy images image enhancement pelican optimization algorithm incomplete beta function segmented gamma correction function

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Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on pelican optimization algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartA期214卷

作者： Alamir, Nehmedo Kamel, Salah Megahed, Tamer F. Hori, Maiya Abdelkader, Sobhy M. Egypt Japan Univ Sci & Technol Elect Power Engn New Borg El Arab City 21934 Egypt Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Mansura Univ Fac Engn Dept Elect Engn Mansoura 35516 Egypt Tottori Univ Environm Studies 1-1-1 Wakabadai Kita Tottori 6891111 Japan

This paper proposes a new application of pelican optimization algorithm (POA) for optimal Energy Management (EM) in Microgrid (MG) considering Demand Response program (DRP). To maximize the MG operator (MGO) benefit and to reduce the overall operating cost, including the cost of conventional generator fuel and power transaction cost, multi-objective optimization is formulated. To achieve the optimal operation of the MG, a Hybrid DRP is proposed, based on Incentive-based Demand response (IDR) to reduce the peak load and to ensure MG reliability. Reliability is achieved by applying the Hybrid technique to encourage customers to reduce their consumption during peak hours. Applying the conventional IDR (CIDR) for optimal operation leads to customers curtailments to be in off-peak hours. Also instead of using predetermined or specifying fixed hours as peak hours, the proposed hybrid DR technique is a dynamic technique based on the load profile and the average load value. Furthermore, Peak reduction percentage (PRP) was employed to show MG's reliability enhancement. Two distinct MG test systems are examined;the effectiveness of the proposed hybrid dynamic demand response (HDDR) with the proposed POA is demonstrated by comparing its simulation results to those of well-known metaheuristics and newly developed algorithms. According to HDDR with POA technique results, a total reduction in peak hours' load is about 14.6% in the first test system and 7.6% in the second test system. The results indicate that the POA has superiority in solving the EM problem.

关键词： Energy Management Hybrid Demand Response Microgrid optimization pelican optimization algorithm

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Enhanced pelican optimization algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks

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SENSORS 2023年第18期23卷 7711页

作者： Wang, Zhen Duan, Jin Xu, Haobo Song, Xue Yang, Yang Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130022 Peoples R China

In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced pelican optimization algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 x 100 m2. These nodes were categorized into four heterogeneous scenarios: m=0,alpha=0, m=0.1,alpha=2, m=0.2,alpha=3, and m=0.3,alpha=1.5. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.

关键词： heterogeneous wireless sensor networks pelican optimization algorithm energy efficient cluster head selection

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A solution to multi Objective Stochastic Optimal Power Flow problem using mutualism and elite strategy based pelican optimization algorithm

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APPLIED SOFT COMPUTING 2024年 158卷

作者： Dora, Bimal Kumar Bhat, Sunil Halder, Sudip Srivastava, Ishan Visvesvaraya Natl Inst Technol Nagpur 440010 India Babasaheb Bhimrao Ambedkar Univ UIET Dept Elect Engn Lucknow 226012 India VNIT Elect Engn Dept Nagpur 440010 India

Wind Energy is rapidly growing into one of the most popular energy options for power generation. However, the unpredictability of wind necessitates the employment of a distribution function when describing the wind environment. In the depiction of wind speeds, the two significant parameters of the Weibull distribution, such as the scale parameter and the shape parameter are often utilized. Therefore, it is essential to choose the most appropriate approach for estimating these parameters. Furthermore, the uncertainty in delegated power from variable wind speeds makes system management challenging. In this paper, the Weibull distribution parameters were determined using the pelican optimization algorithm (POA). POA has gained extensive popularity among the research community and is currently used to address a wide range of optimization problems. However, the exploration and exploitation are not properly balanced in this algorithm. To overcome this problem a novel hybrid algorithm namely Mutualism phase based POA (MPOA) is proposed with the help of mutualism phase of Symbiotic Organisms Search (SOS) algorithm. To demonstrate the efficacy and robustness MPOA is applied on multi-objective Optimal Power Flow (MO-OPF) problems. For this study IEEE 30 bus standard system and modified IEEE 30 bus system consisting of two wind farms are taken. The purpose of this work is to examine the impact of changing the Weibull parameters, reverse and penalty cost coefficients on the overall generation cost while considering all the security aspects.

关键词： Multi-objective optimal power flow Metaheuristic algorithm pelican optimization algorithm Wind energy Weibull distribution

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An Improved pelican optimization algorithm Based on Chaos Mapping Factor

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ENGINEERING LETTERS 2023年第4期31卷 1605页

作者： Li, Yutong Liu, Yu Lin, Chunli Wen, Jiayao Yan, Pengguo Wang, Yukun Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

pelican optimization algorithm (POA) is a meta-heuristic algorithm that emulates the behavior and hunting strategy of pelicans. This paper presents three modifications that enhance the algorithmic traversal of POA by incorporating ten chaotic mapping factors. These modifications aim to improve the efficiency and accuracy of the traversal process. Firstly, the pelican optimization algorithm with chaotic mapping factors added to population generation but random prey generation (COPOA);secondly, the pelican optimization algorithm with chaotic mapping factors applied to prey generation but random population generation (OCPOA);finally, the pelican optimization algorithm with the same chaotic mapping factor, different sequences generated by different random initial values are mapped to the population and prey generation (CCPOA). The results of simulations on 12 benchmark test functions demonstrate that the algorithm (COPOA) adding the chaotic mapping factor to the population generation alone has higher generalizability, convergence speed and finding accuracy compared with the standard POA algorithm. Among these modifications, the Sine-COPOA method performed the best in ten chaotic mapping factor comparison tests. Furthermore, a comparison is made between the Sine-COPOA and typical intelligent optimization algorithms to assess its competence in optimization. The results show that Sine-COPOA performs better in most of the 12 benchmark test functions. Finally, the engineering design problems (pressure vessel problem and 10-bar truss design) are optimized. The experimental results demonstrate that Sine-COPOA effectively solves both function optimization and engineering optimization problems.

关键词： pelican optimization algorithm Chaotic mapping factor Metaheuristic algorithm Traversal

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Efficient DC motor speed control using a novel multi-stage FOPD(1+PI) controller optimized by the pelican optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Jabari, Mostafa Ekinci, Serdar Izci, Davut Bajaj, Mohit Zaitsev, Ievgen Sahand Univ Technol Fac Elect Engn Tabriz Iran Batman Univ Dept Comp Engn TR-72100 Batman Turkiye Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Graph Era Dept Elect Engn Dehra Dun 248002 India Graph Era Hill Univ Dehra Dun 248002 India Univ Business & Technol Coll Engn Jeddah 21448 Saudi Arabia Natl Acad Sci Ukraine Inst Electrodynam Dept Theoret Elect Engn & Diagnost Elect Equipment Beresteyskiy 56 UA-03680 Kiev Ukraine Natl Acad Sci Ukraine Ctr Informat Analyt & Tech Support Nucl Power Faci Akad Palladina Ave 34-A Kiev Ukraine

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the pelican optimization algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

关键词： DC motor control Fractional-order controller Metaheuristic algorithms pelican optimization algorithm Performance optimization PID controller Speed control Stability improvement

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Research on Jamming Resource Allocation Based on Improved pelican optimization algorithm

Research on Jamming Resource Allocation Based on Improved Pe...

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第二十一届分布式计算及其应用国际学术研讨会

作者： Junwei Zhao Yian Liu Hailing Song Jiangnan University School of AI and Computer Science

To get the best jamming effect of limited jamming resources in electronic warfare,this paper focuses on the problem of jamming resource *** objective function of jamming resource allocation is built,which is based on the calculation by the probability formula of j ammer pressing,and an improved pelican optimization algorithm is *** simulating the pelican's natural behavior of approaching prey and searching the nearby area in the process of hunting,the algorithm creates the corresponding mathematical model,and obtains the global optimal allocation strategy by solving the *** simulated results indicate that the probability of obtaining the optimal solution and optimal allocation strategy of the algorithm are better than the traditional Simulated Annealing algorithm,whether it is the situation of "single jammer to single radar" or "multijammer to single-radar".

关键词： component resource allocation radar jamming swarm intelligence algorithm pelican optimization algorithm

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pelican optimization algorithm with Deep Learning for Aspect based Sentiment Analysis on Asian Low Resource Languages

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Hussein Ali Rasool Firas Abedi Ayad Ghany Ismaeel Ali Hashim Abbas Raed Khalid Ahmed Alkhayyat Mustafa Musa Jaber Ankit Garg Altoosi University College Najaf Iraq Department of Mathematics College of Education Al-zahraa University for women Karbala Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna 66002 Iraq Medical technical college Al-Farahidi University Baghdad Iraq College of technical engineering The Islamic University Najaf Iraq Department of Medical instruments engineering techniques Al-Turath University College Baghdad10021 Iraq Department of Medical instruments engineering techniques Dijlah University Baghdad10021 Iraq Apex Institute of Technology CSE Chandigarh University Mohali Punjab University Centre for Research & Development Chandigarh University Mohali Punjab India 140413

Recently, sentiment analysis (SA) has become more popular as it is crucial to moderate and examine the data from the internet. It contains several applications, such as social media monitoring, market research, and opinion mining. Aspect Based Sentiment Analysis (ABSA) is a domain of SA that manages sentiment at a better level. ABSA classifies sentiment in terms of all the aspects for obtaining superior insights as sentiment expressed. A major contribution has been developed in ABSA, and then this progress can be restricted only to some languages with suitable resources. One common method is to utilize machine learning (ML) approaches, namely Neural Networks (NN), Support Vector Machines (SVM), and Naive Bayes (NB), together with Asian and low language-specific resources. These resources offer data on the sentiment polarity (neutral, positive, or negative) of phrases and words that are generally utilized in low-resource languages. In this aspect, this study develops a new pelican optimization algorithm with Deep Learning for ABSA (POADL-ABSA) on Asian and Low Resource Languages. The proposed POADL-ABSA technique focuses on the detection and classification of sentiments. To accomplish this, the POADL-ABSA technique encompasses various levels of operations such as pre-processed, feature vector conversion, and classification. In addition, the POADL-ABSA technique employs the BERT model for feature vector extraction. Besides, attention-based bi-directional long short-term memory (ABiLSTM) system was used for the recognition and classification of sentiments. Finally, the POA was utilized for optimum hyperparameter selection of the ABiLSTM model, and it helps in attaining enhanced sentiment classification results. To ensure the improvised performance of the IAOADL-ABSA technique, an extensive experimental outcome the IAOADL-ABSA technique surpassed other models with \(acc{u}_y\), \(pre{c}_n\), \(rec{a}_l\), and \({F}_{score}\) of 98.72%, 98.71%, 98.72%, and 98.71%, respec

关键词： Natural language processing Asian and low resource language Aspect based sentiment analysis pelican optimization algorithm Deep learning

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