9 results on '"manta ray foraging optimization algorithm"'
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2. PERFORMANCE EVALUATIONS OF THE MANTA RAY FORAGING OPTIMIZATION ALGORITHM IN REAL-WORLD CONSTRAINED OPTIMIZATION PROBLEMS.
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YILDIZDAN, Gülnur
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OPTIMIZATION algorithms , *METAHEURISTIC algorithms , *PERFORMANCE evaluation , *SWARM intelligence , *CONSTRAINED optimization - Abstract
Metaheuristic algorithms are often preferred for solving constrained engineering design optimization problems. The most important reason for choosing these algorithms is that they guarantee a satisfactory response within a reasonable time. The swarm intelligence-based manta ray foraging optimization algorithm (MRFO) is a metaheuristic algorithm proposed to solve engineering applications. In this study, the performance of MRFO is evaluated on 19 mechanical engineering optimization problems in the CEC2020 real-world constrained optimization problem suite. In order to increase the MRFO performance, three modifications are made to the algorithm; in this way, the enhanced manta ray foraging optimization (EMRFO) algorithm is proposed. The effects of the modifications made are analyzed and interpreted separately. Its performance has been compared with the algorithms in the literature, and it has been shown that EMRFO is a successful and preferable algorithm for this problem suite. [ABSTRACT FROM AUTHOR]
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- 2024
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3. Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features.
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Liu, Xiyuan, Wang, Liying, Yan, Hongyan, Cao, Qingjiao, Zhang, Luyao, and Zhao, Weiguo
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ARTIFICIAL neural networks , *OPTIMIZATION algorithms , *MOBULIDAE , *FUZZY algorithms , *DISCRETE wavelet transforms , *DRAFT tubes , *MACHINE learning - Abstract
To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Gene Selection for Microarray Cancer Classification based on Manta Rays Foraging Optimization and Support Vector Machines.
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Houssein, Essam H., Hassan, Hager N., Al-Sayed, Mustafa M., and Nabil, Emad
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TUMOR classification , *SUPPORT vector machines , *MOBULIDAE , *RAYS (Fishes) , *CLASSIFICATION algorithms - Abstract
In DNA microarray applications, many techniques are proposed for cancer classification in order to detect normal and cancerous humans or classify different types of cancers. Gene selection is usually required as a preliminary step for a cancer classification problem. This step aims to select the most informative genes among a great number of genes, which represent an important issue. Although many studies have been proposed to address this issue, they lack getting the most informative and fewest number of genes with the highest accuracy and little effort from the high dimensionality of microarray datasets. Manta ray foraging optimization(MRFO) algorithm is a new meta-heuristic algorithm that mimics the nature of manta ray fishes in food foraging. MRFO has achieved promising results in other fields, such as solar generating units. Due to the high accuracy results of the support vector machines (SVM), it is the most commonly used classification algorithm in cancer studies, especially with microarray data. For exploiting the pros of both algorithms (i.e., MRFO and SVM), in this paper, a hybrid algorithm is proposed to select the most predictive and informative genes for cancer classification. A binary microarray dataset, which includes colon and leukemia1, and a multi-class microarray dataset that includes SRBCT, lymphoma, and leukemia2, are used to evaluate the accuracy of the proposed technique. Like other optimization techniques, MRFO suffers from some problems related to the high dimensionality and complexity of the microarray data. For solving such problems as well as improving the performance, the minimum redundancy maximum relevance (mRMR) method is used as a preprocessing stage. The proposed technique has been evaluated compared to the most common cancer classification algorithms. The experimental results show that our proposed technique achieves the highest accuracy with the fewest number of informative genes and little effort. [ABSTRACT FROM AUTHOR]
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- 2022
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5. Carbon emissions prediction considering environment protection investment of 30 provinces in China.
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Zhao, Kai, Yu, Shujiang, Wu, Lifeng, Wu, Xu, and Wang, Lan
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CARBON emissions , *SUSTAINABLE investing , *OPTIMIZATION algorithms , *ENVIRONMENTAL protection , *CARBON offsetting , *PUBLIC investments , *MOBULIDAE - Abstract
In the backdrop of carbon peaking and carbon neutrality, carbon emissions have always been a major concern. The approach of the heterogeneity grey model is proposed, aiming to predict carbon emissions of 30 provinces in China. This model combines the manta ray foraging optimization algorithm to search for the optimal heterogeneity coefficient. By using the heterogeneity grey model, the carbon emissions are analyzed in 30 provinces of China from 2022 to 2030 considering different environmental protection investment scenarios. The results indicate that in 19 provinces from 2022 to 2030, there is a significant decrease in carbon emissions as government investment increases. In 11 provinces during the same period, there is a rising trend in carbon emissions with the increase of government investment. Hence, achieving a reduction in carbon emissions necessitates not only relying on government investment in environmental protection but also exploring alternative approaches to mitigate carbon emissions. The methodologies and conclusions proposed in this study can provide technical references and making decision references for provincial carbon emission efforts. • The carbon emissions are analyzed in 30 provinces of China considering environmental protection investment. • The approach of the heterogeneity grey model is proposed. • There is a rising trend in carbon emissions with the increase of government investment in 11 provinces. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Experimental investigation of influence of phase change materials in energy consumption of air-conditioning units and prediction performance evaluation of modified deep neural network model.
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Rajesh, B. and Mekala, C.
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ARTIFICIAL neural networks , *PHASE change materials , *MORTAR , *ENERGY consumption , *STANDARD deviations , *AIR conditioning , *PALMITIC acid - Abstract
• Hybrid paraffin-halloysite-ethylene glycol phase change micro capsules are prepared. • The impact of PHEg micro-capsules on tested building's energy usage was studied. • Experiment was successfully predicted by using modified teaching and learning model. 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 × 1.22 m × 0.2 m size test specimens were prepared to experiment. After testing the specimens with various outside temperatures, 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 °C. In addition, the performances of prepared 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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7. Reducing fuel cost and enhancing the resource utilization rate in energy economic load dispatch problem.
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Chen, Chao, Qu, Linan, Tseng, Ming-Lang, Li, Lingling, Chen, Chih-Cheng, and Lim, Ming K.
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FUEL costs , *ENERGY consumption , *MOBULIDAE , *DIFFERENTIAL evolution , *MATHEMATICAL optimization , *MICROGRIDS - Abstract
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 manta ray foraging optimization (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. [ABSTRACT FROM AUTHOR]
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- 2022
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8. Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm.
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Zahedi Vahid, Masoud, Ali, Ziad M., Seifi Najmi, Ebrahim, Ahmadi, Abdollah, Gandoman, Foad H., and Aleem, Shady H. E. Abdel
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POWER resources , *DISTRIBUTED power generation , *MOBULIDAE , *SMART power grids , *MATHEMATICAL optimization , *PARTICLE swarm optimization , *ENERGY dissipation - Abstract
In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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9. Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO).
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Hemeida, Mahmoud G., Alkhalaf, Salem, Mohamed, Al-Attar A., Ibrahim, Abdalla Ahmed, and Senjyu, Tomonobu
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MATHEMATICAL optimization , *MOBULIDAE , *PARTICLE swarm optimization , *BEES algorithm , *ALGORITHMS , *LEVY processes , *BUS transportation - Abstract
Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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