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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.

The rise of Electric Vehicles (EVs) has introduced significant advancement and evolution in the electricity market. In smart transportation, the EVs have earned more popularity because of its numerous benefits including lower carbon footprints, higher performance, and sophisticated energy trading mechanisms. These potential benefits have resulted in widespread EV adoption across the world. Despite its benefits, energy management remains the biggest challenge in EVs and it is mainly because of the lack of Charging Stations (CSs) near EVs. This creates a demand for an effective, secure and reliable energy management framework for EVs. This study presents a secure data and energy trade paradigm based on Blockchain (BC) in the Internet of EVs (IoEV). BC technology prepares for the high volume of EV integration that serves as the foundation for the next generation, and to assist in developing unique privacy-protected BC-based D-Trading and storage Models. Entities evaluated for the proposed model include Trusted Authority (TA), Vehicles, Smart Meters, Roadside Units (RSU), BC, and Inter-Planetary File System (IPFS). In addition, E-trading involves several phases, including the acquiring E-trading demand requests, E-trading response requests, request matching and token assignment. Moreover, account mapping is performed using a Mayfly Pelican Optimization Algorithm (MPOA), which is created by merging the Mayfly Algorithm (MA) and Pelican Optimization Algorithm (POA). Various security features are used to protect data and energy trade in IoEV, including encryption, hashing, polynomials, and others. The testing results revealed that the MPOA outperformed the state-of-the-art results regarding memory consumption, trading rate, transaction cost, and trading energy volume with values of 4.605 MB, 91%, 0.654, and 90 kW, respectively.

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 × 100 m2. These nodes were categorized into four heterogeneous scenarios: m=0,α=0, m=0.1,α=2, m=0.2,α=3, and m=0.3,α=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.

Fuel cells are a newly developed source for generating electric energy. These cells produce electricity through a chemical reaction between oxygen and hydrogen, which releases electrons. In recent years, extensive research has been conducted in this field, leading to the emergence of high-power batteries. This study introduces a novel technique to enhance the power quality of grid-connected proton-exchange membrane (PEM) fuel cells. The proposed approach uses an inverter following a buck converter that reduces voltage. A modified pelican optimization (MPO) algorithm optimizes the controller firing. A comparison is made between the controller’s performance, based on the recommended MPO algorithm and various other recent approaches, demonstrating the superior efficiency of the MPO algorithm. The study’s findings indicate that the current–voltage relationship in proton-exchange membrane fuel cells (PEMFCs) follows a logarithmic pattern, but becomes linear in the presence of ohmic overvoltage. Furthermore, the PEMFC operates at an impressive efficiency of 60.43% when running at 8 A, and it can deliver a significant power output under specific operating conditions. The MPO algorithm surpasses other strategies in terms of efficiency and reduction in voltage deviation, highlighting its effectiveness in managing the voltage stability, and improving the overall performance. Even during a 0.2 sagging event, the MPO-based controller successfully maintains the fuel cell voltage near its rated value, showcasing the robustness of the optimized regulators. The suggested MPO algorithm also achieves a superior accuracy in maintaining the voltage stability across various operating conditions.

The rapid development of low earth orbit (LEO) satellite constellations for navigation augmentation represents significant challenges in optimizing coverage performance while minimizing system complexity. A hybrid optimization algorithm based on pelican optimization algorithm and quantum particle swarm optimization (POA-QPSO) is proposed in this paper for multi-objective optimization design of dual-layer Walker constellations. The algorithm integrates the global search capability of the POA and the local exploitation ability of QPSO, effectively balancing exploration and exploitation through a probability-driven dual-phase search mechanism, a three-tier adaptive parameter adjustment strategy, and a pareto frontier maintenance mechanism. Probability factor and quantum tunneling facilitate low-cost deep search in complex non-convex environments. Experiments demonstrate that the algorithm outperforms MOPOA and MOPSO on ZDT test functions, with an 18.5% improvement in IGD metrics. In LEO constellation optimization, the designed dual-layer configuration (800 km/144 satellites in the first layer and 1426 km/56 satellites in the second layer) achieves a 92.7% global coverage, with an average PDOP of 1.78 and 5.8 visible satellites in polar regions. Furthermore, comparative benchmark tests show that the proposed solution outperforms most mainstream algorithms and performs better than traditional medium Earth orbit satellite systems in mid-to-high latitude regions. This research provides an efficient solution for LEO navigation augmentation system design.

Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications.

This paper proposes an improved Pelican optimization algorithm (IPOA) based on comprehansive strategy for the parameter identification of photovoltaic models. Firstly, the cubic chaotic mapping and the refraction reverse learning strategy are used to initialize the pelican population and enhance its diversity. Secondly, the position update formula of the Pelican optimization algorithm in the global detection phase is replaced by the position update formula of the red-tailed Eagle optimization algorithm in the soaring phase to obtain the adequacy of the Pelican optimization algorithm in solution space search. Further introducing the catchy variation strategy aims to improve the algorithm’s global search ability. Finally, the reverse solution generated by the lens imaging principle can provide a new search direction through the mirror reverse learning strategy when the Pelican optimization algorithm falls into the local optimal. The CEC2022 test function performed analysis and comparison with eight meta-heuristic algorithms. The Wilcoxon rank sum test verified the significance of the algorithm. In addition, the IPOA was used to optimize the critical parameters of the PV model to solve the problem of actual parameter identification of the single-diode and double-diode photovoltaic module models. The experimental results indicate that the IPOA outperforms other classical swarm intelligence algorithms in both convergence speed and solving accuracy. Furthermore, this optimization method yields the smallest mean square error across all types of solar cells, demonstrating the superiority of the proposed algorithm.

With the rapid advancement of agricultural technology, Agricultural Wireless Sensor Network (AWSN) monitoring for crop growth in large-scale fields has become pivotal in smart agriculture. Optimizing AWSN coverage is crucial for enhancing production efficiency and resource utilization. However, traditional optimization algorithms struggle with local convergence and accuracy in large-scale sensor deployment, an NP-hard problem. To address this, we propose a novel Multi-Strategy Pelican Optimization Algorithm (MSPOA), integrating a good point set strategy, a 3D spiral Lévy flight strategy, and an adaptive T-distribution variation strategy. The good point set strategy expands the search range and enhances local search capability, while the 3D spiral Lévy flight strategy improves convergence speed and global search accuracy. The adaptive T-distribution variation strategy further boosts global search ability, and pelican-inspired movement and collaboration strategies enhance adaptability and robustness in diverse agricultural scenarios. Comparative experiments with Improved Artificial Bee Colony Algorithm (IABC), Chaotic Adaptive Firefly Optimization Algorithm (CAFA), Adaptive Particle Swarm Optimization (APSO), and Lévy Flight Strategy Chaotic Snake Optimization Algorithm (LCSO) demonstrate that MSPOA improves network coverage by 5.85%, 11.33%, 21.05%, and 20.66%, respectively. Additionally, MSPOA exhibits strong adaptability and stability in dynamic agricultural environments.

The Internet of Things (IoT) network is a fast-growing technology, which is efficiently used in various applications. In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communication challenges. The platform of IoT uses a cloud service as a backend for processing information and maintaining remote control. To manage the developing intricacy of cyberattacks, it is critical to have an effectual intrusion detection system (IDS), which can monitor computer sources and create data on suspicious or abnormal actions. The IoT network’s security can progressively become a critical concern as IoT technology obtains extensive use. Protecting IoT systems with traditional IDS is challenging due to the vast variety and volume of IoT devices. Currently, Machine Learning (ML) and Deep Learning (DL) techniques are utilized to address the security threats in IoT networks. This manuscript proposes a Cybersecurity through an Attention-based Stacked Autoencoder with a Pelican Optimization Algorithm for the Detection and Mitigation of Attacks (CASAE-POADMA) methodology on an IoT-assisted network. The main purpose of the CASAE-POADMA methodology is to identify and mitigate the presence of cybersecurity attack behavior in the IoT-assisted network. At first, the presented CASAE-POADMA approach utilizes min–max normalization to scale input data into a uniform design. Besides, the greylag goose optimization (GGO) method is employed for the feature selection process. For the detection and mitigation of attack, the presented CASAE-POADMA approach employs the attention-based stacked autoencoder (ASAE) method. Eventually, the hyperparameter tuning of the ASAE method is executed by using pelican optimization algorithm (POA) method. The simulation validation of the CASAE-POADMA approach is verified under a benchmark database. The experimental validation of the CASAE-POADMA approach exhibited a superior accuracy value of 99.50% over existing techniques.

As a classification model, a broad learning system is widely used in wind turbine fault diagnosis. However, the setting of hyperparameters for the models directly affects the classification accuracy of the models and it generally relies on practical experience and prior knowledge. In order to effectively solve the problem, the parameters of the broad learning system such as the number of feature nodes, the number of enhancement nodes, and the number of mapped features layer were optimized by the improved pelican optimization algorithm, and a classification model was built based on the broad learning system optimized by the improved pelican optimization algorithm. The classification accuracy of the proposed model was the highest and reached 98.75%. It is further shown that compared with the support vector machine, deep belief networks, and broad learning system models optimized by particle swarm optimization algorithm, the proposed model effectively improves the accuracy of wind turbine fault diagnosing.