RESUMO
The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)-a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance-capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems.
RESUMO
Vector-borne diseases are a major burden to human health. It accounts for more than 17% of the total infectious diseases and causes more than 0.7 million deaths annually. Mosquitoes are potential vectors for many vector-borne diseases that cause illness to public health, globally. Vector species of the genus Aedes i.e., Aedes aegypti and Aedes albopictus are the vector for many arboviruses such as dengue, chikungunya, yellow fever, and Zika in India. Dengue is one of the most prevalent viral infections causing a high number of cases throughout the world and resistance to insecticides can be a reason for the failure of vector control strategies. This study was carried out to check the degree of resistance among these vectors in the Chittorgarh district of Rajasthan, India through standard World Health Organization protocol. The resistance was monitored to pyrethroids i.e., deltamethrin (0.05%), permethrin (0.75%), alphacypermethrin (0.05%); organochlorine i.e., DDT (4%), and an organophosphate larvicide i.e., temephos (0.02 mg/L) in both vector species. Complete resistance to DDT was observed among all tested populations of both species. All tested populations of Aedes albopictus were found susceptible to pyrethroids. Aedes aegypti was found resistant in the Mangalwad population, unconfirmed resistant in Bhopalsagar and Rashmi populations while the remaining are susceptible to permethrin. The Mangalwad population was also found unconfirmed resistant to deltamethrin and alphacypermethrin. Larvae of both species were found susceptible to temephos. Decreasing the use of DDT will help to reduce the impact on human health and environmental contamination. However, temephos as a larvicide, deltamethrin, and alphacypermethrin as an adulticide can be used in critical disease outbreaks at a minimum concentration as mosquitoes are found susceptible in the study area.
RESUMO
Parameters for defining photovoltaic models using measured voltage-currentâ characteristics are essential for simulation, control, and evaluation of photovoltaic-based systems. This paper proposes an enhanced chaotic JAYA algorithm to classify the parameters of various photovoltaic models, such as the single-diode and double-diode models, accurately and reliably. The proposed algorithm introduces a self-adaptive weight to regulate the trend to reach the optimal solution and avoid the worst solution in various phases of the search space. The self-adaptive weight capability also allows the proposed technique to reach the best solution at the earliest phase, and later, the local search process starts, which also increase the ability to explore. A three different chaotic process, including sine, logistics and tent map, is proposed to optimize the consistency of each generation's best solution. The proposed algorithm and its variants proposed are used to solve the parameter estimation problem of various PV models. To show the proficiency of the suggested algorithm and its variants, an extensive simulation is carried out using MATLAB/Simulink software. Two statistical tests are conducted and compared with the latest techniques for validating the performance of the suggested algorithm and its variants. Comprehensive analysis and experimental results display that the suggested algorithm can achieve highly competitive efficiency in terms of accuracy and reliability compared to other algorithms in the literature. This research will be backed up with extra online service and guidance for the paper's source code at https://premkumarmanoharan.wixsite.com/mysite.
