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An efficient manta ray foraging optimization algorithm with individual information interaction and fractional derivative mutation for solving complex function extremum and engineering design problems

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Liu, Jingsen Chen, Yang Liu, Xiaoyu Zuo, Fang Zhou, Huan Henan Univ Henan Int Joint Lab Theories & Key Technol Intelli Kaifeng Peoples R China Henan Univ Coll Software Kaifeng Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou Peoples R China Henan Univ Business Sch Kaifeng Peoples R China

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

关键词： manta ray foraging optimization algorithm Information interaction Fractional derivative mutation optimization-seeking accuracy Engineering design optimization problems

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Modified manta ray foraging optimization algorithm based improved load frequency controller for interconnected microgrids

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IET RENEWABLE POWER GENERATION 2022年第16期16卷 3587-3613页

作者： Ahmed, Emad M. Selim, Ali Mohamed, Emad A. Aly, Mokhtar Alnuman, Hammad Ramadan, Husam A. Jouf Univ Dept Elect Engn Sakaka Saudi Arabia Aswan Univ Dept Elect Engn Fac Engn Aswan Egypt Univ San Sebastian Fac Ingn Arquitectura & Diseno Santiago Chile Minia Univ Fac Engn Elect Engn Dept Al Minya Egypt

The recent environmental crisis and global warming have compelled an energy transition, particularly in the power generation and transportation sectors. Increased energy competence and production will necessitate enormous efforts. Admittedly, power system inertia metrics have revealed reduced inertia attributes, which could lead to a catastrophic shutdown and security issues in future electrical power systems. The design of the load frequency controller is essential for improving frequency regulation and power system stability. Nonetheless, more research is needed into control development and design approaches that take into account renewable energy characteristics, complexities, connected electric vehicles (EVs), and uncertainties. Metaheuristic-based design methods and fractional order control have recently demonstrated an improved response when compared to classical design methods and integer-order controllers. This work presents an improved fractional order hybrid control system based on two modified versions of the manta ray foraging optimization (MRFO) algorithm. The exploration and exploitation stages of the modified MRFO are enhanced by utilizing a chaotic map and a weighting factor. The transients and steady-state performance of the studied two-areas interconnected microgrids have been significantly improved by incorporating the advantages of both the suggested fractional order-based control approach and the proposed modified MRFO algorithms. While assessing the feasibility of the developed controller and modified optimizers, uncertainty, renewable energy fluctuations, load transients, and electric vehicle attributes are all properly considered. The modified MRFO's performances are evaluated using 23 benchmark functions, and the results are compared to the original MRFO and recent well-known optimization algorithms. Both statistical analysis and time-domain results demonstrate the superiority of the proposed modified optimizers and the proposed load frequen

关键词： electric vehicle attributes statistical analysis distributed power generation two-areas interconnected microgrids security issues renewable energy fluctuations electric vehicles MRFO algorithms power generation transportation sectors classical design methods optimisation environmental crisis fractional order hybrid control system control system synthesis load regulation load frequency controller account renewable energy characteristics energy transition frequency regulation metaheuristic-based design methods frequency control power system inertia metrics power system interconnection power system control load transients manta ray foraging optimization algorithm catastrophic shutdown renewable energy sources integer-order controllers fractional order control fractional order-based control approach global warming power system stability

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Deep multi-scale three-dimensional convolutional neural network optimized with manta ray foraging optimization algorithm for classification of lung cancer on CT images

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IMAGING SCIENCE JOURNAL 2024年第6期72卷 723-735页

作者： Nair, Veena V. Vinitha, C. S. Shajin, Francis H. Carl von Ossietzky Univ Oldenburg Neurosci Dept Oldenburg Germany NSUT East Campus Dept Elect & Commun Engn Delhi India Xpertmindz Innovat Solut Pvt Ltd Res & Dev Kuzhithurai Tamil Nadu India NSUT East Campus Dept Elect & Commun Engn Delhi 110031 India

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.

关键词： Deep multi scale three-dimensional convolutional neural networks lung cancer classification computed tomography manta ray foraging optimization algorithm residual exemplars local binary pattern switched mode fuzzy median filter

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Research on Energy Storage Locating and Sizing based on Improved manta ray foraging optimization algorithm 2

Research on Energy Storage Locating and Sizing based on Impr...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Hu, Xuming Li, Zhan Hu, Nan Xu, Yuanyuan Meng, Gaojun Nantong Elect Power Design Inst Co Ltd Nantong Peoples R China Nanjing Inst Technol Nanjing Peoples R China

ISBN: (纸本)9798350349047;9798350349030

Energy storage has the characteristics of strong flexibility and fast response, which can effectively alleviate the load fluctuation, voltage instability, harmonic pollution and other problems caused by the high proportion of new energy access. In this paper, a power system energy storage locating and sizing based on bi-level model and improved manta ray foraging optimization algorithm is proposed. First of all, a bi-level locating and sizing model is established to minimize energy storage investment cost, daily average voltage fluctuation and daily average load fluctuation. Secondly, the elite reverse learning strategy and adaptive tumbling factor are used to improve the manta ray foraging optimization algorithm, and the improved algorithm is used to solve the model. Finally, the proposed method is simulated by taking the IEEE39 node system with high proportion of new energy as an example. The results prove that the proposed method has faster operation speed and higher accuracy, and the locating and sizing selection scheme can effectively reduce the system investment cost and significantly reduce the system voltage and load fluctuation.

关键词： Energy Storage Locating and Sizing Bi-level optimization algorithm manta ray foraging optimization algorithm High Proportion of New Energy Grid Connection

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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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ENERGIES 2021年第16期14卷 4856-4856页

作者： Vahid, Masoud Zahedi Ali, Ziad M. Najmi, Ebrahim Seifi Ahmadi, Abdollah Gandoman, Foad H. Aleem, Shady H. E. Abdel Univ Sistan & Baluchestan Dept Elect & Comp Engn Zahedan *** Iran Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Elect Engn Dept Wadi Addawaser 11991 Saudi Arabia Roshdieh Higher Institue Educ Tabriz *** Iran Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Vrije Univ Brussel Res Grp MOBI Mobil Logist & Automot Technol Res C B-1050 Brussels Belgium Sci Valley Acad Valley Higher Inst Engn & Technol Dept Elect Engn & Elect Al Qalyubia 44971 Egypt

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.

关键词： smart distribution network distributed generation with scheduling capability power generation resources manta ray foraging optimization algorithm

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An improved binary manta ray foraging optimization algorithm based feature selection and random forest classifier for network intrusion detection

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2022年 16卷

作者： Hassan, Ibrahim Hayatu Abdullahi, Mohammed Aliyu, Mansur Masama Yusuf, Sahabi Ali Abdulrahim, Abdulrazaq Ahmadu Bello Univ Dept Comp Sci Zaria Nigeria Kebbi State Univ Sci & Technol E Lib Aliero Nigeria

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.

关键词： manta ray foraging optimization algorithm Intrusion detection Anomaly detection Feature selection Classification Random forest

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Deep multi-scale three-dimensional convolutional neural network optimized with manta ray foraging optimization algorithm for classification of lung cancer on CT images

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The Journal of Photographic Science 2024年第6期72卷

作者： Veena V. Nair C. S. Vinitha Francis H. Shajin a Neuroscience Department University of Oldenburg Oldenburg Germany b Department of Electronics and Communication Engineering NSUT-East Campus Delhi India c Research and Development Xpertmindz Innovative Solutions Private Limited Kuzhithurai Tamil Nadu India

ABSTRACTLung 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.

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Distributed Generators optimization Based on Multi-Objective Functions Using manta rays foraging optimization algorithm (MRFO)

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ENERGIES 2020年第15期13卷 3847-3847页

作者： Hemeida, Mahmoud G. Alkhalaf, Salem Mohamed, Al-Attar A. Ibrahim, Abdalla Ahmed Senjyu, Tomonobu Minia Higher Inst Engn Dept Elect Engn Al Minya 61111 Egypt Qassim Univ Dept Comp Sci Arrass Coll Sci & Arts Qasim 51431 Saudi Arabia Aswan Univ Dept Elect Engn Fac Engn Aswan 81511 Egypt Univ Ryukyus Fac Engn 1 Senbaru Nakagami 90370213 Japan

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 Levy Flights algorithm (QODELFA), Stochastic Fractal Search algorithm (SFSA), Genetics algorithm (GA), Comprehensive Teaching Learning-Based optimization (CTLBO), Comprehensive Teaching Learning-Based optimization (CTLBO (epsilon 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.

关键词： optimization techniques manta ray foraging optimization algorithm multi-objective function radial networks optimal power flow

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Gene Selection for Microarray Cancer Classification based on manta rays foraging optimization and Support Vector Machines

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第2期47卷 2555-2572页

作者： Houssein, Essam H. Hassan, Hager N. Al-Sayed, Mustafa M. Nabil, Emad Minia Univ Fac Comp & Informat Al Minya Egypt Cairo Univ Fac Comp & Artificial Intelligence Giza Egypt Islamic Univ Madinah Fac Comp Sci & Informat Syst Madinah Saudi Arabia

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.

关键词： Microarray Gene expression Gene selection Cancer classification Feature selection manta ray foraging optimization algorithm Support vector machines Minimum Redundancy Maximum Relevance

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Carbon emissions prediction considering environment protection investment of 30 provinces in China

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ENVIRONMENTAL RESEARCH 2024年 244卷 117914页

作者： Zhao, Kai Yu, Shujiang Wu, Lifeng Wu, Xu Wang, Lan Hebei Univ Engn Sch Management Engn & Business Handan 056038 Peoples R China Hebei Univ Engn Hebei Key Lab Intelligent Water Conservancy Handan 056038 Peoples R China Hebei Handan Hydrol Survey Res Ctr Handan 056003 Peoples R China Handan Univ Coll Econ & Management Handan 056005 Peoples R China

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.

关键词： Carbon emission Environmental protection investment Heterogeneity grey prediction model manta ray foraging optimization algorithm

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