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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]

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]

Lung cancer starts in the lungs and spreads to other organs in the body. Premature identification can only help the doctor to make an exact diagnosis and it may save the life of patients. Numerous studies have been conducted in this area, but none of them attains the accuracy outcomes. To overcome this drawback, a deep multi-scale three dimensional convolutional neural network optimized with the manta ray foraging optimization algorithm is proposed in this article for lung cancer classification on CT images (LCCT-DMS3DCNN-MRFOA) which effectively classifies the lung cancer as benign or malignant. The simulation is executed in MATLAB. The ELCAP dataset is utilized to confirm the performance of the proposed LCCT-DMS3DCNN-MRFOA approach. The efficiency of the LCCT-DMS3DCNN-MRFOA approach attains 14.21%, 22.96% and 24.94% higher accuracy; 12.59%, 11.71% and 11.55% higher AUC and 59.83%, 53.05% and 61.41% lower computational time compared with existing methods. [ABSTRACT FROM AUTHOR]

Economic Load Dispatch (ELD) is an effective dispatch strategy to improve economic efficiency while ensuring the safety and stability of the power system. It minimizes production costs by rationally allocating the power generated by each power system unit. In this paper, an Manta Ray Foraging Optimization (MRFO) algorithm based on the mathematical spiral foraging strategy is proposed to solve the ELD problem in power systems considering transmission losses. Eight different mathematical spirals were introduced into the MRFO algorithm's foraging strategy, including the Rose spiral, Archimedes spiral, Fermat spiral, Cycloid spiral, Hypotrochoid spiral, Epitrochoid spiral, Inverse spiral and Lituus spiral. The mathematical spiral foraging strategy can enhance the global search ability of the MRFO algorithm and improves its convergence velocity. To verify the performance of the proposed improved MRFO algorithm, 30 benchmark functions are tested and the optimization performances are compared with BOA, AOA, SCA, HHO, WOA, RSA, and GWO. The simulation experimental results show that the performance and application of the manta ray foraging optimization algorithm based on the mathematical spiral foraging strategy outperform other intelligent optimization algorithms tested on 30 benchmark functions. Finally, two ELD cases with total demands of 2500 MW and 10500 MW are selected and solved using the improved manta ray foraging optimization algorithm. Comparing the simulation results with other optimization algorithms, it is shown that the proposed improved algorithm obtains the best fuel cost and smaller transmission loss in almost every test case, which canl help to improve the economic efficiency of the power system and achieve the goal of economic load dispatch.

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.

Economic Load Dispatch (ELD) is an effective dispatch strategy to improve economic efficiency while ensuring the safety and stability of the power system. It minimizes production costs by rationally allocating the power generated by each power system unit. In this paper, an Manta Ray Foraging Optimization (MRFO) algorithm based on the mathematical spiral foraging strategy is proposed to solve the ELD problem in power systems considering transmission losses. Eight different mathematical spirals were introduced into the MRFO algorithm's foraging strategy, including the Rose spiral, Archimedes spiral, Fermat spiral, Cycloid spiral, Hypotrochoid spiral, Epitrochoid spiral, Inverse spiral and Lituus spiral. The mathematical spiral foraging strategy can enhance the global search ability of the MRFO algorithm and improves its convergence velocity. To verify the performance of the proposed improved MRFO algorithm, 30 benchmark functions are tested and the optimization performances are compared with BOA, AOA, SCA, HHO, WOA, RSA, and GWO. The simulation experimental results show that the performance and application of the manta ray foraging optimization algorithm based on the mathematical spiral foraging strategy outperform other intelligent optimization algorithms tested on 30 benchmark functions. Finally, two ELD cases with total demands of 2500 MW and 10500 MW are selected and solved using the improved manta ray foraging optimization algorithm. Comparing the simulation results with other optimization algorithms, it is shown that the proposed improved algorithm obtains the best fuel cost and smaller transmission loss in almost every test case, which canl help to improve the economic efficiency of the power system and achieve the goal of economic load dispatch. [ABSTRACT FROM AUTHOR]

The growth within the Internet and communications areas have led to a massive surge in the dimension of network and data. Consequently, several new threats are being created and have posed difficulties for security networks to correctly discover intrusions. Intrusion Detection System (IDs) is one amongst the foremost essential events for security arrangements in network environments, and it is commonly applied to spot, track, and detect malevolent threats. Detecting intruders using metaheuristics and machine learning methodologies in recent trend offers improved discovery rate. Therefore, this paper presented an intrusion detection model using an improved Binary Manta Ray Foraging (BMRF) Optimization Algorithm based on adaptive S-shape function and Random Forest (RF) classifier. The BMFR is envisioned to identify the most relevant features and remove redundant and irrelevant ones from the intrusion detection datasets. Furthermore, the RF is used for feature evaluation and to build the intrusion detection model. The proposed method was validated and compared with other methods using two IDs benchmark datasets, which include NSL-KDD and CIC-IDS2017 datasets. The result indicates that the presented model selected 38 features with 99.6% precision, 94.3% recall, 96.9% f-measure, and 99.3% accuracy for the CIC-IDS2017 dataset. Moreover, for the NSL-KDD dataset, the presented model selected 22 features with 96.8%, 96.2%, 96.5%, and 98.8% for precision, recall, F-measure, and accuracy. In addition, a statistical significance test reveals a significance difference between the presented model and the compared methods in terms of F-measure.

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]

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]

The manta ray foraging optimization algorithm (MRFO) is a recently proposed meta-heuristic algorithm that mimics the foraging process of manta rays. It has yielded good outcomes in solving some optimization problems because its mechanism is clear, no additional parameters need to be set, and the balance between global and local search is good. Nonetheless, while dealing with high-dimensional global optimization and complex engineering optimization problems, there are also issues such as premature convergence, low optimization-seeking accuracy, or unstable solutions. To this end, this article proposes an efficient manta ray foraging optimization algorithm (NIFMRFO) by incorporating individual information interaction and fractional derivative mutation. First, to prevent premature convergence of the algorithm, a nonlinear cosine adjustment parameter is presented, which is intended to make the demand relationship between global exploration and local development more reasonable. Then, an information interaction strategy among random individuals is employed to expedite the rate at which the algorithm converges. Finally, a fractional derivative mutation strategy is utilized to continually enhance individuals' quality in each iteration, which not only increases the population diversity but also helps to improve the precision and stability of the search results. Theoretical analysis indicates that the improved NIFMRFO algorithm and basic MRFO algorithm have the same time complexity. In simulation experiments, the CEC2017 suite is used to conduct comparison tests with six superior-performance representative comparison algorithms in several dimensions. In terms of the optimization-seeking accuracy, convergence curve, violin plot, and Friedman average ranking, the analysis of these graphs and data shows that the NIFMRFO algorithm's ameliorated strategy improves superiority-seeking power, convergence speed, and steadiness. Meanwhile, the Wilcoxon rank-sum test result illustrates significant differences between NIFMRFO and other compared algorithms. Finally, these algorithms are utilized to tackle seven realistic engineering design optimization problems. The result makes it clear that NIFMRFO is distinctly superior to the other six algorithms, showing that its solving ability is superior and has broad application prospects. • A novel manta ray foraging optimization NIFMRFO with three improved strategies is proposed. • The execution efficiency of NIFMRFO is demonstrated by time complexity analysis. • The performance of NIFMRFO is verified by the CEC2017 test suite. • Multiple statistical analysis and chart analysis methods are used. • NIFMRFO's application capability is evaluated by seven engineering constraint problems. [ABSTRACT FROM AUTHOR]

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.

• 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]

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.

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]

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]

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]