Search Results (3)

This article is positioned into a cross-section of machine learning, cybersecurity, and nature-inspired domains. This article’s main objective is to use eXtended Classifier System (XCS), a known adaptive Reinforcement Learning (RL) algorithm, and alter it to use the Bacterial Foraging Optimization Algorithm (BFOA) instead of its original Genetic Algorithm component. This modification transforms XCS into a multi-criteria optimization system (BFOA-XCS) through evaluation of classifier fitness across accuracy, stability, and variance reduction while simultaneously using weighted-sum scalarization. In this way, the method leverages BFOA’s chemotactic search and population dynamics. The proposed BFOA-XCS integration was validated in two experimental phases. First, evaluations across 19 benchmark machine learning datasets demonstrated that Improved BFOA (IBFOA)-XCS achieves the best Friedman ranking among all XCS variants (marginally significant at α = 0.10, supported by medium-to-large effect sizes), with notable variance reduction (15.2 percent) over standard GA-XCS. Second, in a dynamic cybersecurity simulation environment with six attack scenarios, all XCS variants significantly outperformed three of five deep RL baselines (Deep Q-Network (DQN), Q-Learning, and Policy Gradient (REINFORCE)) with large statistical effect sizes. Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC) achieved higher overall rewards but at substantially greater computational cost: PPO at 5.3× and SAC at 26.1× the XCS compute time per run (2 min 8 s and 10 min 26 s, respectively, vs. 24 s for XCS). The results demonstrate that rule-based XCS with BFOA optimization offers a compelling alternative to neural approaches for cybersecurity defense, combining competitive performance with interpretable policies and substantially lower computational requirements. Full article

Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R²) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting. Full article

This paper suggests an innovative approach for the ideal placement and categorization of capacitors in radial distribution networks (RDNs) by applying symmetric fuzzy and improved bacterial foraging optimization algorithm (IBFOA) solutions. The reactive power reimbursement significantly enhances the function of the power system, and capacitor placement is an impressive technique used to reduce loss of the system. The capacitor allocation for distribution system problems involves determining the ideal location and size of the capacitor. In this work, load flow is performed at first to compute actual losses and voltages at different nodes without compensation. In the planned technique, the loss sensitivity factor (VSF) and voltage stability index (VSI) are utilized to determine the optimal location of capacitors in RDNs. Here, the IBFOA is used to determine the proper rating of the capacitor. The suggested scheme is applied on three different types of RDNs. Full article