Publications Scientifiques

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Author: search.filters.author.Rezoug, AbdellahHas files: No

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    MOGA: Multi-Objective Genetic Algorithm to select Stacking Ensemble Learning for classification
    (Elsevier, 2025) Rezoug, Abdellah; Bader-el-den, Mohamed
    Stacking Ensemble Learning (SEL) has been effectively integrated with Multi-Objective Optimisation (MOO) heuristics for classification tasks across various domains, including finance, healthcare, and cybersecurity. This study aims to address the challenge of generalising SEL to a diverse set of classification cases. Thus, the Multi-Objective Genetic Algorithm (MOGA) framework is proposed, utilising a Genetic Algorithm (GA) to evolve a population of distinct SELs, each built from a varied subset of base models. The goal is to select the subset that composes the most effective SEL for a given classification task. MOGA is designed with two main objectives—maximising precision and recall—which helps to maintain independence from any specific classification case. In addition, incorporating models of varied types ensures adaptability and high performance in different situations. Comprehensive experimentation was conducted on 23 diverse datasets, where MOGA demonstrated high performance in nearly all datasets, outperforming other ensemble learning (EL) methods in 100% of the datasets in precision, 78% in recall, 69.5% in f1−score, and 78% in accuracy. A t-test analysis yielded results of p-value <0.05, indicating a statistically significant improvement in the accuracy of the MOGA over the base models. Moreover, the framework's application can be extended to regression tasks as well
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    Optimisation of fuzzy logic quadrotor attitude controller–particle swarm, cuckoo search and BAT algorithms
    (Taylor & Francis, 2021) Zatout, Mohamed Siddiq; Rezoug, Amar; Rezoug, Abdellah; Baizid, Khalifa; Iqbal, Jamshed
    Bio-inspired optimisation algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviours of some animal species in such a way that allows solving optimisation problems. The present paper aims at applying three metaheuristic methods for optimising fuzzy logic controllers used for quadrotor attitude stabilisation. The investigated methods are particle swarm optimisation (PSO), BAT algorithm and cuckoo search (CS). These methods are applied to find the best output distribution of singleton membership functions of the fuzzy controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: integral squared error, integral time-weighted absolute error and integral time-squared error. These metrics allow performance comparison of the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than CS. According to these results, the BAT-based fuzzy controller exhibits superior performance compared with other algorithms to stabilise the quadrotor