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Subject: search.filters.subject.Hybrid algorithms

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    Intelligent path planning algorithms for UAVs: Classification, complexity analysis, hybrid ablation insights, and future directions
    (SAGE, 2025) Dradoum, Alaa; Khelassi, Abdelmadjid; Lachekhab, Fadhila
    As unmanned aerial vehicle (UAV) technology has evolved, these systems are being increasingly utilized across diverse industries. However, controlling UAVs faces significant problems owing to several environmental circumstances and obstacles, making path planning a critical initial step for UAV operation. This paper offers an overview of UAV path planning research founded on intelligent algorithms, which are divided into three categories: computational intelligence (CI), machine learning (ML), and hybrid methods. Each category has been analyzed in depth to show its strengths, limits, and where it may be applied to UAV-related problems. The methodology includes a comparative analysis based on multiple performance metrics such as path length, flight time, collision avoidance, complexity, and environmental adaptability. Furthermore, the research covers the latest publications that deal with solving essential challenges of UAV path planning by using new hybrid algorithms and enhanced optimization methods. The results indicate that although each strategy offers specific strengths suited to particular scenarios, hybrid strategies are more likely to deliver greater flexibility and robustness, particularly in uncertain, and dynamic environments. These findings are significant for guiding future research in adaptive path planning and for supporting practical UAV applications such as autonomous delivery, aerial surveillance, disaster response, and environmental monitoring.
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    An enhanced battery model using a hybrid genetic algorithm and particle swarm optimization
    (Springer Nature, 2023) Mammeri, Elhachemi; Ahriche, Aimad; Necaibia, Ammar; Bouraiou, Ahmed; Mekhilef, Saad; Dabou, Rachid; Ziane, Abderrezzaq
    Batteries are widely used for energy storage in stand-alone PV systems. However, both PV modules and batteries exhibit nonlinear behavior. Therefore, battery modeling is an essential step toward appropriate battery control and overall PV system management. Empirical models remain reliable for lead-acid batteries, especially the Copetti model, which describes many inner and outer battery phenomena, including temperature dependency. However, the parameters of the Copetti model require further adjustment to increase its ability to accurately represent battery behavior. Recently, metaheuristic algorithms have been employed for parameter identification, especially hybrid algorithms that combine the advantages of two or more algorithms. This paper proposes an enhanced battery model based on the Copetti model. The parameter identification of the enhanced model has been carried out using a novel hybrid PSO-GA algorithm (HPGA). The hybrid algorithm combines GA and PSO in a cascade configuration, with GA as the master algorithm. The HPGA algorithm has been compared with other algorithms, namely GA, PSO, ABC, COA, and a hybrid GWO-COA, to reveal its advantages and disadvantages. The NRMSE is used to evaluate algorithms in terms of tracking speed and efficiency. HPGA shows an improvement in tracking efficiency compared to GA and PSO. The proposed model is validated on several charging-discharging data and exhibits a 15% lower mean error compared to the Copetti model with original parameters. Additionally, the proposed model demonstrates a lower mean error of 0.16% compared to other models in the literature with a 0.36% mean error at least.
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    A novel approach for global solar irradiation forecasting on tilted plane using hybrid evolutionary neural networks
    (Elsevier, 2021) Amiri, Billel; Gómez-Orellana, Antonio M.; Gutiérrez, Pedro Antonio; Dizène, Rabah; Hervás-Martínez, César; Dahmani, Kahina
    An efficient management of solar power systems requires direct and continuous predictions of global irradiation received on inclined planes. This paper proposes a new approach that simultaneously estimates and forecasts inclined solar irradiation. The method is based on a multi-task Hybrid Evolutionary Neural Network with two output neurons: one estimates the irradiation at the current instant and another predicts it for the next hour. An Evolutionary Algorithm is used to learn the most proper topology (number of neurons and connections). Two studies are carried out to evaluate the performance of the method, considering experimental ground data for the same inclination angle and satellite data with different tilt angles. The data only contain one measured variable, what improves its applicability to other sites. The potential of three different basis functions in the hidden layer is compared (Sigmoidal Units, Radial Basis Functions and Product Units), concluding that the results achieved by Sigmoidal Units are better. Single and multi-task models are also compared with a statistical analysis, which shows no significant differences. However, the proposed multi-task option is much simpler and computationally efficient than individual models. The problem tackled is more complex and challenging than previous works, due to inclined solar irradiation is predicted based on the horizontal irradiation and also because the model simultaneously estimates and predicts irradiation. However, the performance obtained is excellent compared to the literature
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    Signal processing deployment in power quality disturbance detection and classification
    (2017) Dekhandji, Fatma Zohra
    Power quality disturbances have adverse impacts on the electric power supply as well as on the customer equipment. Therefore, the detection and classification of such problems is necessary. In this paper, a fast detection algorithm for power quality disturbances is presented. The proposed method is a hybrid of two algorithms, abc–0dq transformation and 90 phase shift algorithms. The proposed algorithm is fast and reliable in detecting most voltage disturbances in power systems such as voltage sags, voltage swells, voltage unbalance, interrupts, harmonics, etc. The three-phase utility voltages are sensed separately by each of the algorithms. These algorithms are combined to explore their individual strengths for a better performance. When a disturbance occurs, both algorithms work together to recognize this distortion. This control method can be used for critical loads protection in case of utility voltage distortion. Simulation and analysis results obtained in this study illustrate high performance of the strategy in different single-phase and three-phase voltage distortions
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    Hybrid harmony search combined with stochastic local search for feature selection
    (Springer, 2015) Nekkaa, Messaouda; Boughaci, Dalila