Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimizationalgorithm is proposed to solve various benchmark fun...
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Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimizationalgorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the prairie dog optimization algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose optimizationalgorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods;these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.
In this article, a new prairie dog optimization algorithm (PDOA) is analyzed to realize the optimum economic design of three well-known heat exchangers. These heat exchangers found numerous applications in industries ...
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In this article, a new prairie dog optimization algorithm (PDOA) is analyzed to realize the optimum economic design of three well-known heat exchangers. These heat exchangers found numerous applications in industries and are an imperative part of entire thermal systems. optimization of these heat exchangers includes knowledge of thermo-hydraulic designs, design parameters and critical constraints. Moreover, the cost factor is always a challenging task to optimize. Accordingly, total cost optimization, including initial and maintenance, has been achieved using multi strategy enhanced PDOA combining PDOA with Gaussian mutation and chaotic local search (MSPDOA). Shell and tube, fin-tube and plate-fin heat exchangers are a special class of heat exchangers that are utilized in many thermal heat recovery applications. Furthermore, numerical evidences are accomplished to confirm the prominence of the MSPDOA in terms of the statistical results. The obtained results were also compared with the algorithms in the literature. The comparison revealed the best performance of the MSPDOA compared to the rest of the algorithm. The article further suggests the adaptability of MSPDOA for various real-world engineering optimization cases.
The prairiedogoptimization (PDO) algorithm is a metaheuristic optimizationalgorithm that simulates the daily behavior of prairiedogs. The prairiedog groups have a unique mode of information exchange. They divide ...
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The prairiedogoptimization (PDO) algorithm is a metaheuristic optimizationalgorithm that simulates the daily behavior of prairiedogs. The prairiedog groups have a unique mode of information exchange. They divide into several small groups to search for food based on special signals and build caves around the food sources. When encountering natural enemies, they emit different sound signals to remind their companions of the dangers. According to this unique information exchange mode, we propose a randomized audio signal factor to simulate the specific sounds of prairiedogs when encountering different foods or natural enemies. This strategy restores the prairiedog habitat and improves the algorithm's merit-seeking ability. In the initial stage of the algorithm, chaotic tent mapping is also added to initialize the population of prairiedogs and increase population diversity, even use lens opposition-based learning strategy to enhance the algorithm's global exploration ability. To verify the optimization performance of the modified prairie dog optimization algorithm, we applied it to 23 benchmark test functions, IEEE CEC2014 test functions, and six engineering design problems for testing. The experimental results illustrated that the modified prairie dog optimization algorithm has good optimization performance.
prairiedogoptimization is a population-based optimization method that uses the behavior of prairiedogs to find the optimal solution. This paper proposes a novel optimization method, called the Opposition-based Lapl...
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prairiedogoptimization is a population-based optimization method that uses the behavior of prairiedogs to find the optimal solution. This paper proposes a novel optimization method, called the Opposition-based Laplacian Distribution with prairiedogoptimization (OPLD-PDO), for solving industrial engineering design problems. The OPLD-PDO method combines the concepts of opposition-based Laplacian distribution and prairiedogoptimization to find near-optimal solutions. This causes faster convergence to the optimal solution and reduces the chances of getting stuck in a local minimum. The OPLD-PDO method was tested on several benchmark problems to validate its performance. The results were compared with other methods, and the OPLD-PDO method was superior regarding solution quality. The results of this study demonstrate the potential of the OPLD-PDO method as a useful tool for solving industrial engineering design problems and photovoltaic (PV) solar problems. & COPY;2023 Elsevier B.V. All rights reserved.
Classification of motor imagery using electroencephalography (EEG-MI) is an integral part of the brain-computer interface (BCI), which helps those with mobility impairments connect with the outside world. Unfortunatel...
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Organ transplantation is the ultimate option to treat terminal illness by transplanting the deceased or damaged organs with healthy organs for improving the patient's life expectancy. The number of organs needed a...
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Organ transplantation is the ultimate option to treat terminal illness by transplanting the deceased or damaged organs with healthy organs for improving the patient's life expectancy. The number of organs needed and the organs available for transplantation vary enormously. The tremendous advancements in utilizing big data analytics in the healthcare system make it efficient to explore decision-making information. To make optimal decisions in organ transplantation, this paper proposes a modified convolutional neural network-hybrid extreme learning machine (MCNN-HELM) based prediction model. The proposed MCNN-HELM model utilizes three different real-time datasets as inputs which contain records of liver, heart, and lung transplantation details of the donor and recipient. At first, the missing values and inaccurate data present in real-time datasets are removed via pre-processing. The pre-processed data are then trained using the MCNN-HELM model that efficiently determines the suitable donor for the recipient by minimizing the waiting time of the recipient for the matching organ donor. Moreover, the MCNN-HELM model gives initial preference to patients with high-risk rates to improve their quality of life. The proposed MCNN-HELM model achieves training accuracy of 97.5% with a computational time of 2.2 s, while the precision value of estimated factual outcomes, potential outcomes, and the accuracy of the best donor type are obtained by 16.3582, 16.1401, and 0.6784 which are more efficient than other state-of-the-art methods.
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