In this paper, the recently developed optimization algorithm, namely equilibrium optimization (EO), will be utilized to solve the optimal power flow problem (OPF), combining stochastic wind power with conventional the...
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In this paper, the recently developed optimization algorithm, namely equilibrium optimization (EO), will be utilized to solve the optimal power flow problem (OPF), combining stochastic wind power with conventional thermal power generators in the system. The objectives are to minimize generation costs, including those incurred in thermal and stochastic wind power generation, active power loss, voltage deviation, and emission. To evaluate the performance of the EO algorithm in the OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems with stochastic wind power generators will be used. A comparative study will be performed to show the efficiency of the EO algorithm compared with other recently developed metaheuristic algorithms such as the marine predators algorithm (MPA), artificial ecosystem-based optimization (AEO), and slime mould algorithm (SMA), as well as with other well-known algorithms. based on the obtained results, the EO algorithm offered the best results.
Cloud-fog computing frameworks are emerging paradigms developed to add benefits to the current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a key role, and the optimized schedule o...
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Cloud-fog computing frameworks are emerging paradigms developed to add benefits to the current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a key role, and the optimized schedule of IoT task requests can improve system performance and productivity. In this paper, we developed an alternative task scheduling technique for IoT requests in a cloud-fog environment based on a modified artificial ecosystem-based optimization (AEO), called AEOSSA. This modification is developed using the operators of the Salp Swarm Algorithm (SSA) in an attempt to enhance the exploitation ability of AEO during the process of finding the optimal solution for the problem under consideration. The performance of the designed AEOSSA approach to tackling the task scheduling problem is evaluated using different synthetic and real-world datasets of different sizes. In addition, a comparison is conducted between AEOSSA and other well-known metaheuristic methods for performance investigation. The experimental results demonstrate the high ability of AEOSSA to tackle the task scheduling problem and perform better than other methods according to the performance metrics such as makespan time and throughput. (C) 2021 Elsevier B.V. All rights reserved.
This paper studies the electrical properties (water absorption, dielectric strength) of (Epoxy) and Ethylene Propylene Rubber (EPR) blend with different content under different environment conditions at atmospheric te...
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ISBN:
(纸本)9781665408738
This paper studies the electrical properties (water absorption, dielectric strength) of (Epoxy) and Ethylene Propylene Rubber (EPR) blend with different content under different environment conditions at atmospheric temperature. the results of the test showed improvement in the dielectric strength with increasing the EPR content in the blends samples and a decrease in water absorption. artificial ecosystem-based optimization (AEO) and Manta ray foraging optimization (MRFO) are optimization approaches for solving optimization problems. They were used to determine the optimal value of the percentage of EPR in the Blend parameter for improving the electrical properties of Epoxy and EPR blend with different content under different environment conditions at atmospheric temperature. The AEO algorithm was a fast and effective method at finding the best values of the percentage of EPR in the Blend parameter comparing with the MRFO technique.
In this study, the performance of a solar thermoelectric air-conditioning system (STEACS) is predicted using advanced optimized artificial intelligence models. A STEACS powered by solar PV panels is experimentally tes...
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In this study, the performance of a solar thermoelectric air-conditioning system (STEACS) is predicted using advanced optimized artificial intelligence models. A STEACS powered by solar PV panels is experimentally tested under different cooling loads varying from 65.0 to 260 W. The obtained experimental data are used to train and test the model. The model consists of a random vector functional link (RVFL) network optimized by one metaheuristic optimizer such as jellyfish search algorithm (JFSA), artificial ecosystem-based optimization (AEO), manta ray foraging optimization (MRFO), and sine cosine algorithm (SCA). The inputs of the model were time, solar irradiance, ambient temperature, wind speed, and humidity. The predicted responses of the investigated system are the input current of PV, the average temperature of the air-conditioned room, the cooling capacity, and the coefficient of performance. The accuracy of the four models is evaluated using eight statistical measures. RVFL-JFSA outperformed the other models in predicting all responses with a correlation coefficient of 0.948-0.999 and, consequently, it is recommended to use it to model STEACS system.
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