Yazar "Hu, Gang" seçeneğine göre listele

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    Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning
    (Elsevier Science Inc, 2024) Hu, Gang; Huang, Feiyang; Seyyedabbasi, Amir; Wei, Guo
    The path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.
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    V-shaped and S-shaped binary artificial protozoa optimizer (APO) algorithm for wrapper feature selection on biological data
    (Springer, 2025) Seyyedabbasi, Amir; Hu, Gang; Shehadeh, Hisham A.; Wang, Xiaopeng; Canatalay, Peren Jerfi
    This study addresses the limitation of feature selection (FS) problems in high-dimensional biomedical datasets. The high-dimensional datasets contain attributes that are deemed irrelevant, redundant, and noisy. Thus, the process of feature selection is a valuable initial step aimed at improving the performance of classification models through the identification and selection of a constrained set of significant and impactful features. Due to the NP-hard nature of feature selection, it is crucial to recognize that addressing these challenges requires the utilization of metaheuristic algorithms. However, since the feature selection problem is a discrete problem, the binary version of metaheuristic algorithms should be used. To overcome these challenges, this paper proposes a novel bAPO algorithm that leverages adaptive population dynamics for more efficient exploration and exploitation of the solution space. The proposed bAPO algorithm uses V-shaped and S-shaped transfer functions to obtain wrapper feature selection in biological data. There are eight different versions of the bAPO algorithm in this study that were evaluated with 14 well-known biological datasets. The obtained results have been analyzed with the fitness value, the number of selected features, k-nearest neighbors (KNN) accuracy, support vector machine (SVM) accuracy, and random forest (RF). Statistical validation using p-value analysis demonstrates the robustness and reliability of the results. The obtained findings suggest that the proposed bAPO algorithm provides a powerful method for tackling optimization problems, particularly in high-dimensional datasets. In fitness performance, the bAPO-V1 and bAPO-V2 (27.70%) demonstrate superior performance, and in terms of reduced features, the bAPO-V2 (36.36%) algorithm achieved good performance.