To tackle the shortcomings of the Dung Beetle optimization (DBO) algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, and susceptibility to local optima, a Somersault For...
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To tackle the shortcomings of the Dung Beetle optimization (DBO) algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, and susceptibility to local optima, a Somersault foraging and Elite Opposition-Based Learning Dung Beetle optimization (SFEDBO) algorithm is proposed. This algorithm utilizes an elite opposition-based learning strategy as the method for generating the initial population, resulting in a more diverse initial population. To address the imbalance between exploration and exploitation in the algorithm, an adaptive strategy is employed to dynamically adjust the number of dung beetles and eggs with each iteration of the population. Inspired by the mantarayforagingoptimization (MRFO) algorithm, we utilize its somersault foraging strategy to perturb the position of the optimal individual, thereby enhancing the algorithm's ability to escape from local optima. To verify the effectiveness of the proposed improvements, the SFEDBO algorithm is utilized to optimize 23 benchmark test functions. The results show that the SFEDBO algorithm achieves better solution accuracy and stability, outperforming the DBO algorithm in terms of optimization results on the test functions. Finally, the SFEDBO algorithm was applied to the practical application problems of pressure vessel design, tension/extension spring design, and 3D unmanned aerial vehicle (UAV) path planning, and better optimization results were obtained. The research shows that the SFEDBO algorithm proposed in this paper is applicable to actual optimization problems and has better performance.
This study contributes for solving the economic load dispatch (ELD) problem to reduce the energy waste caused by thermal generation units and promotes cleaner and sustainable production in the power industry. Electric...
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This study contributes for solving the economic load dispatch (ELD) problem to reduce the energy waste caused by thermal generation units and promotes cleaner and sustainable production in the power industry. Electricity is produced by thermal power plants;however, thermal power generation involves low economic benefits and high pollution levels, which hinders cleaner and sustainable production in the power industry. An improved mantarayforagingoptimization (IMRFO) algorithm is developed for solving the ELD problem and realizing the cleaner and economic goal of the thermal units. The characteristics of the novel method present that: (1) Sine and cosine adaptations were introduced in the manta ray foraging optimization algorithm to enhance its adaptive ability;(2) a nonlinear convergence factor was presented to enhance the convergence speed;and (3) a differential evolution algorithm was introduced in the original algorithm to enhance robustness. Three typical ELD test systems were selected to prove the feasibility of the IMRFO-based solution method. The results indicated that IMRFO algorithm obtained the most competitive scheduling strategy compared with the existing methods. Improving the economy of power system operation is beneficial to realize cleaner and sustainable power production.
In this study, the influence of hybrid phase change materials on the energy consumption of air-conditioning units installed to maintain a comfortable temperature inside the testing room was experimentally studied and ...
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In this study, the influence of hybrid phase change materials on the energy consumption of air-conditioning units installed to maintain a comfortable temperature inside the testing room was experimentally studied and numerically analyzed. The Paraffin, Halloysite and Ethylene glycol microcapsules were combined to form PHEg filler materials. These PHEg phase change materials were further mixed into a normal Portland cement mortar to prepare 5 different phase change material mortars. Based on the prepared motors, a pair of 1.22 m x 1.22 m x 0.2 m size test specimens were prepared to experiment. After testing the specimens with various outside tem-peratures, the energy consumed by an air-conditioning unit was reported. There was a 20-22% reduction in energy consumption recorded while testing with phase change material mortar specimens than normal cement mortar. Also, the results proved that the energy consumption of the air-conditioning unit could be reduced further by 10% when the outdoor temperature dropped to below 24 degrees C. In addition, the performances of pre-pared samples were predicted using a modified deep neural network model. The developed model accumulated 99.867% accuracy and a maximum of 0.1607 mean absolute error and 0.0172 root mean square error, better than several existing neural network models.
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