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By combining opposition-based learning techniques with conventional African Vulture Optimization (AVO), this study offers a notable improvement in the handling of optimization problems. Despite the limitations of AVO, such as issues involving extremely rough search spaces, more iterations or function evaluations are necessary. To overcome this limitation, our proposed paper, an enhanced opposition-based learning (EOBL), speeds up the convergence and, at the same time, assists the algorithm in escaping local optima. A combination of this new technique with AVO, the Enhanced Opposition-based African Vulture Optimizer (EOBAVO), is proposed. The performance of the suggested EOBAVO was evaluated through experiments using the CEC2005 and CEC2022 benchmark functions in addition to seven engineering challenges. Furthermore, statistical analyses, including the t-test and Wilcoxon rank-sum test, were conducted, and they demonstrated that the proposed EOBAVO surpasses several of the leading algorithms currently in use. The results indicate that the proposed approach can be regarded as a competent and efficient solution for complex optimization challenges.

This study presents a Chaos-driven Optimization (CDO) algorithm, a novel metaheuristic algorithm designed to enhance global search performance, convergence speed, and solution robustness. The algorithm incorporates chaos theory by initially evaluating ten distinct chaotic maps as candidate generators, from which the Logistic map is ultimately selected for the final CDO implementation based on systematic performance analysis, enabling an effective balance between exploration and exploitation while reducing premature convergence. The effectiveness of CDO is evaluated using 35 standard benchmark functions, including 23 from CEC-2005 and 12 from CEC-2022. Comparative analysis against state-of-the-art optimizers demonstrates that CDO consistently outperforms or achieves competitive results. Statistical validation using mean and standard deviation, complemented by Friedman ranking and post-hoc Nemenyi significance analysis, confirms the proposed algorithm’s statistically consistent superiority and robustness. To demonstrate practicability, CDO is applied to the optimal sizing of an off-grid Hybrid Renewable Energy Systems (HRES) for the rural community of Abrafo, Ghana, using one year of real load and meteorological data. The system integrates photovoltaic arrays, wind turbines, biomass generators, battery storage, electrolyzers, hydrogen tanks, and fuel cells. Optimization is performed under multiple reliability levels defined by the Loss of Power Supply Probability (LPSP). Results show that CDO produces economically viable, technically feasible, and reliable system configurations, highlighting its strong potential for sustainable off-grid planning.

The Artificial rabbit optimization (ARO) algorithm replicates the survival skills of rabbits in the wild. However, like other metaheuristic approaches it possesses significant drawbacks in solving challenging problems, including sluggish convergence rate, poor exploration ability and trapped in local optima region. To alleviate these shortcomings, a novel strategy, namely Modified Random Opposition (MRO) and ten chaotic maps are integrated with ARO, termed as MROCARO. This implementation MRO technique boost the population diversity and permits the population to escape from local optima while integration of chaotic map enhances the exploitation capability. To estimate the effectiveness of the MROCARO method, the well-known CEC2005, CEC2017, CEC2019 and CEC2008lsgo test functions are considered. Moreover, non-parametric tests that include the Wilcoxon rank-sum and Friedman rank test are performed to analyze the significant difference among the compared algorithms. Furthermore, the efficiency of the MROCARO algorithm has been evaluated on various structural problems and optimal sizing of renewable energy systems. The experimental findings demonstrate that MROCARO performed optimum solution with 100% renewable sources with the lowest levelized cost of electricity of 0.0934 $/kWh as compared to other methods. Also, the simulation findings reveal that MROCARO has immense potential for addressing global optimization and structural problems as contrasted to other competing algorithms.

Nonlinearity constraints are inherent in all physical systems and can impact the system output. The windup issue occurs when actuators reach their limits, causing a disparity between the system input and the controller output. To eliminate or minimize the impact of saturation, controllers are designed with anti-windup techniques. This paper proposes a new target loop-based simple analytical design of a fractional-order proportional integral (FOPI) anti-windup controller for non-integer-order (NIO) processes with time delay. Explicit tuning rules in terms of plant parameters are established to meet user-defined criteria such as phase margin ( ϕ m ) and maximum sensitivity ( M s ) . To check the performance and robustness of the proposed control law, case studies are conducted and compared with recently developed control laws. The robustness of the proposed controller is examined with parameter variations. Lastly, real-time validation of the proposed control approach is carried out in a two-tank level loop.

This article presents an advanced continuous wavelet transform (CWT) based approach for fault detection and localization in distribution systems using the artificial neural network (ANN). In this study, CWT extracts distinct features from the transient signals captured from the bus. The derived features are utilized to train and test appropriate ANN architecture in different stages to detect and localize the faults. The proposed scheme provides an optimum method for classification as well as localization of the various kinds of fault with different source short circuit (SSC) level in different locations. The whole detection and localization process consists of several stages. In the first stage, it detects faulty feeder. The faulty line is identified in the second stage. Finally, in the third stage, fault type and fault location are being calculated from the relaying point. The performance of the proposed CWT - ANN based approach is quite promising as compared to traditionally used algorithms. However, a correlation-based feature selection technique is also implemented to reduce training time and improve accuracy. This algorithm is tested in 11 kV radial Indian distribution network but can be applied in other distribution networks also.

Biosurfactants are amphiphilic molecules having hydrophobic and hydrophilic moieties produced by various microorganisms. These molecules trigger the reduction of surface tension or interfacial tension in liquids. A biosurfactant-producing halophile was isolated from Lake Chilika, a brackish water lake of Odisha, India (19°41′39″N, 85°18′24″E). The halophile was identified as Bacillus tequilensis CH by biochemical tests and 16S rRNA gene sequencing and assigned accession no. KC851857 by GenBank. The biosurfactant produced by B. tequilensis CH was partially characterized as a lipopeptide using thin-layer chromatography, Fourier transform infrared spectroscopy, and nuclear magnetic resonance techniques. The minimum effective concentration of a biosurfactant for inhibition of pathogenic biofilm (Escherichia coli and Streptococcus mutans) on hydrophilic and hydrophobic surfaces was found to be 50 μg ml⁻¹. This finding has potential for a variety of applications.