Now showing 1 - 4 of 4
  • Publication
    Correction to: Multiclass feature selection with metaheuristic optimization algorithms: a review
    (2023)
    Akinola, Olatunji O.
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    Ezugwu, Absalom E.
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    Agushaka, Jeffrey O.
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    Abualigah, Laith
      21
  • Publication
    Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems
    (2022) ;
    Ekinci, Serdar
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    Izci, Davut
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    Alsoud, Anas Ratib
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    Abualigah, Laith
    The need for better-performing algorithms to solve real-world power systems engineering problems has always been a challenging topic. Due to their stochastic nature, metaheuristic algorithms can provide better results. Thus, they have a rising trend in terms of investigation. This paper is a further attempt to offer a better optimizing structure, therefore, aims to provide a better-performing algorithm both for designing an appropriate proportional-integral-derivative (PID) controller to effectively operate an automatic voltage regulator (AVR) system and extracting the optimum parameters of a power system stabilizer (PSS) employed in a single-machine infinite-bus (SMIB) power system. Therefore, the paper discusses the development of the Lévy flight-based reptile search algorithm with local search capability and evaluates its potential against challenging power systems engineering optimization problems. The Lévy flight concept is used for better exploration capability in the proposed algorithm, whereas the Nelder-Mead simplex search algorithm is integrated for further exploitation. The latter case is confirmed through 23 benchmark functions with different features using statistical and nonparametric tests. The superiority of the proposed Lévy flight-based reptile search and Nelder-Mead (L-RSANM) algorithm-based PID controller for the AVR system is demonstrated comparatively using convergence, statistical and nonparametric tests along with transient and frequency responses. Besides, it is also assessed against previously reported and different methods, showing further superiority for AVR system control. Furthermore, the extraordinary ability of the L-RSANM algorithm to design an efficient PSS employed in the SMIB power system is demonstrated, as well. In conclusion, the proposed L-RSANM algorithm is shown to be more capable to solve the challenging power systems engineering design problems.
      82  150
  • Publication
    Modified arithmetic optimization algorithm for drones measurements and tracks assignment problem
    (2023) ;
    Abualigah, Laith
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    Barbaresco, Frederic
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    Seghrouchni, Amal El Fallah
    This paper presents efforts to solve the multi-track measurement assignment problem in drone detection and tracking. In many cases, several radars are collectively used to track drones efficiently, generating measurements and several tracks under different circumstances. In this work, several measurements are simulated during a time frame accompanied by the generation of several tracks using the Linear Kalman Filter. The focus is on finding an optimum measurements/track assignment for the simulated measurements and track values. The measurements and track generation are simulated using Stone Soup software. On the other hand, the optimization of the problem is implemented using several evolutionary-based metaheuristic algorithms. This optimization problem is known to be computationally explosive, especially if long time frames are considered. In particular, a new modified method based on the Arithmetic Optimization Algorithm is proposed. The optimization is applied to a formulated cost function that considers uncertainty, false alarms, and existing clutters. Simulations and comparisons show the ability of those evolutionary-based algorithms to solve this kind of problem efficiently. The proposed method obtained promising results compared to other comparative methods used to solve this drone’s measurements and track assignment problem.
      28
  • Publication
    Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization
    (2024) ; ;
    Abualigah, Laith
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    Barbaresco, Frederic
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    Seghrouchni, Amal ElFallah
    This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars’ defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis ). That would cut down the cost of operating extra sensors and then it would be possible to move them to other vicinity.