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Efficiency Optimization of Wireless Power Transfer System for Electric Vehicle Based on Improved marine predators algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第7期24卷 7847-7858页

作者： Yu, Quanyi Lin, Jun Ma, Xilai Li, Bo Xu, Linlin Wang, Tianhao Jilin Univ Coll Instrument Sci & Elect Engn Changchun 130026 Peoples R China First Automobile Workshop FAW Jiefang Commer Vehicle Dev Inst Elect Dept Changchun 130011 Peoples R China

Electric vehicle (EV) is the core part of future automobile technology, and safe and reliable wireless power transfer (WPT) technology is the key link to improve the intelligent driving technology of EV. In this paper, the uncertainty quantification method is proposed to guide the optimization design of WPT structure, so as to improve the efficiency of WPT. First this paper establishes a surrogate model of WPT efficiency based on the adaptive sparse polynomial chaos expansion, and the uncertainty of EVs WPT transmission efficiency is quantified, the computational efficiency is improved by about 8.4 times. Then the surrogate model is combined with the global sensitivity analysis method to quantify the impact of different variables in WPT on efficiency and screen out the variables with greater impact. Finally, this paper uses the improved marine predators algorithm to optimize the selected WPT system structure parameters. Considering the uncertainty, the average efficiency of the optimized WPT system is increased from 73.43% to 94.64%. Compared with other optimization methods, it proves that the method in this paper can optimize WPT more efficiently, and significantly improve the transmission efficiency.

关键词： Adaptive sparse polynomial chaos expansion efficiency optimization electric vehicles marine predators algorithm uncertainty quantification wireless power transfer

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Optimization of suspension of rail vehicles with coil springs using marine predators algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第3期238卷 642-653页

作者： Bizic, Milan Bulatovic, Radovan Petrovic, Dragan Univ Kragujevac Fac Mech & Civil Engn Kraljevo Kraljevo Serbia Univ Kragujevac Fac Mech & Civil Engn Kraljevo Dositejeva 19 Kraljevo 36000 Serbia

The topic of this paper is the usage of the algorithm of marine predator (MPA) in the optimization of the suspension of rail vehicles with coil springs. The aim is to reduce the mass of set of coil springs as the main parts of rail vehicles suspension. The optimized set of coil springs must satisfy the appropriate conditions related to the suspension characteristics, with the aim of achieving the required operation quality and running security of the observed rail vehicle. Starting from the bi-linear characteristic of rail vehicles suspension and the analytical description of its parameters, an optimization problem is formulated. It is composed of six optimization parameters, an objective function and 16 constraints (eight for each coil spring in the set). The developed approach is applied in two specific examples of suspension optimization of four-axled freight wagons, the first with axle load of 200 kN and the second with axle load of 225 kN. The optimization problem is resolved using MPA. The acquired results showed that the given optimization approach with MPA provide a significant mass decrease compared to conventional design method of rail vehicles suspension with coil springs. The mass decrease of one set of coil springs in both examples is about 15.5%. Given that each considered four-axled freight wagon has 16 sets of coil springs, the decrease of the total coil springs mass per wagon in both examples is more than 60 kg. Since certified rail vehicles are commonly produced in large series, the proposed approach can be very significant for increasing profitability in the rail vehicles industry.

关键词： Coil spring optimization suspension optimization rail vehicles suspension marine predators algorithm MPA

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A Hybrid COVID-19 Detection Model Using an Improved marine predators algorithm and a Ranking-Based Diversity Reduction Strategy

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IEEE ACCESS 2020年 8卷 79521-79540页

作者： Abdel-Basset, Mohamed Mohamed, Reda Elhoseny, Mohamed Chakrabortty, Ripon K. Ryan, Michael Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Mansoura Univ Fac Comp & Informat Dakahlia 35516 Egypt Univ New South Wales Sch Engn & IT Capabil Syst Ctr Canberra BC 2610 Canada

Many countries are challenged by the medical resources required for COVID-19 detection which necessitates the development of a low-cost, rapid tool to detect and diagnose the virus effectively for a large numbers of tests. Although a chest X-Ray scan is a useful candidate tool the images generated by the scans must be analyzed accurately and quickly if large numbers of tests are to be processed. COVID-19 causes bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. In this work, we aim to extract rapidly from chest X-Ray images the similar small regions that may contain the identifying features of COVID-19. This paper therefore proposes a hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation. The ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations. RDR works on finding the particles that couldn't find better solutions within a consecutive number of iterations, and then moving those particles towards the best solutions so far. The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms: equilibrium optimizer (EO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), Harris-hawks algorithm (HHA), and salp swarm algorithms (SSA). The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics. In addition, the performance of our proposed model was convergent on all numbers of thresholds level in the Structured Similarity Index Metric (SSIM) and Universal Quality Index (UQI) metrics.

关键词： COVID-19 detection marine predators algorithm ranking-based reduction diversity Kapur's entropy image segmentation

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Parameters extraction of single and double diodes photovoltaic models using marine predators algorithm and Lambert W function

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SOLAR ENERGY 2020年 209卷 674-693页

作者： Ridha, Hussein Mohammed Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Adv Lightning Power & Energy Res Serdang 43400 Malaysia

Efficient and accurate estimation parameters of the PV model from real experimental data is crucial for modeling, increasing investments, and determining the actual performance before installing the PV panels. Recently, several stochastic methods have been proposed to extract the parameters of the PV module/cells optimally. However, some of these approaches have restrictions in terms of exploration and exploitation capacities individually or combined due to their stochastic searching strategies. In this research work, marine Predator algorithm (MPA) is combined with Lambert W function to tackle the parameters extraction optimization problem for the single diode and double diode PV models and called (MPALW). The performance of the MPALW algorithm is tested by using real experimental data at seven sunlight and temperature settings. The MPALW shows an excellent agreement with obtained experimental data compared with other well-published algorithms. The findings of this research show that the MPALW algorithm can be utilized for real engineering applications such as smart grids, energy sector, and fault error detection.

关键词： Modeling of PV module Single diode model Double diode model marine predators algorithm Lambert W function

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MOIMPA: multi-objective improved marine predators algorithm for solving multi-objective optimization problems

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SOFT COMPUTING 2023年第21期27卷 15719-15740页

作者： Hassan, Mohamed H. Daqaq, Fatima Selim, Ali Dominguez-Garcia, Jose Luis Kamel, Salah Minist Elect & Renewable Energy Cairo Egypt Mohammed V Univ Rabat Mohammadia Sch Engineers Lab Study & Res Appl Math Rabat 10090 Morocco IREC Catalonia Inst Energy Res Jardins Dones Negre 12a Sant Adria De Besos 08930 Spain Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt

This paper introduces a multi-objective variant of the marine predators algorithm (MPA) called the multi-objective improved marine predators algorithm (MOIMPA), which incorporates concepts from Quantum theory. By leveraging Quantum theory, the MOIMPA aims to enhance the MPA's ability to balance between exploration and exploitation and find optimal solutions. The algorithm utilizes a concept inspired by the Schrodinger wave function to determine the position of particles in the search space. This modification improves both exploration and exploitation, resulting in enhanced performance. Additionally, the proposed MOIMPA incorporates the Pareto dominance mechanism. It stores non-dominated Pareto optimal solutions in a repository and employs a roulette wheel strategy to select solutions from the repository, considering their coverage. To evaluate the effectiveness and efficiency of MOIMPA, tests are conducted on various benchmark functions, including ZDT and DTLZ, as well as using the evolutionary computation 2009 (CEC'09) test suite. The algorithm is also evaluated on engineering design problems. A comparison is made between the proposed multi-objective approach and other well-known evolutionary optimization methods, such as MOMPA, multi-objective ant lion optimizer, and multi-objective multi-verse optimization. The statistical results demonstrate the robustness of the MOIMPA approach, as measured by metrics like inverted generational distance, generalized distance, spacing, and delta. Furthermore, qualitative experimental results confirm that MOIMPA provides highly accurate approximations of the true Pareto fronts.

关键词： Quantum theory marine predators algorithm Multi-objective optimization Non-dominated solutions Engineering design problems

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An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第20期34卷 18015-18033页

作者： Houssein, Essam H. Emam, Marwa M. Ali, Abdelmgeid A. Minia Univ Fac Comp & Informat Al Minya Egypt

Breast cancer is the second leading cause of death in women;therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA

关键词： Breast cancer classification Deep learning Transfer learning Convolutional neural network marine predators algorithm Opposition-based learning Hyperparameters optimization

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A modified self-adaptive marine predators algorithm: framework and engineering applications

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3269-3294页

作者： Fan, Qingsong Huang, Haisong Chen, Qipeng Yao, Liguo Yang, Kai Huang, Dong Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China Yuan Ze Univ Dept Ind Engn & Management Taoyuan 32003 Taiwan South China Univ Technol Coll Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China

The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability.

关键词： Optimization marine predators algorithm Self-adaptive rules Logistic opposition-based learning

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Finding Community Modules for Brain Networks Combined Uniform Design With marine predators algorithm

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IEEE ACCESS 2024年 12卷 127405-127416页

作者： Wang, Yuxian Feng, Junhong Zhang, Jie Chen, Yongsong Guangdong Songshan Polytech Sch Comp & Informat Engn Shaoguan 512126 Guangdong Peoples R China Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Guangxi Peoples R China

Brain networks are made up of multiple neural units that are interconnected and form neural unit modules. The precise and efficient identification of these modules is crucial for detecting diseases and providing targeted therapy. A novel algorithm, named UMPA, has been developed to analyze brain networks and obtain their neural unit modules. UMPA combines the strengths of uniform design and marine predators algorithm (MPA) to achieve uniformly scattered feasible solutions across the vector domain and quickly find the optimal solution. It is the first method to integrate MPA and uniform design for identifying unit modules in brain networks. The performance of UMPA has been tested on 29 TD resting-state functional MRI brain networks, and the results demonstrate that it outperforms the other five methods in terms of modularity and is comparable in terms of conductance. Furthermore, a comparative analysis of UMPA and MPA indicates that uniform design contributes to the enhancement of UMPA. UMPA has been applied to a variety of brain networks, including those of healthy individuals and those of patients with neurological disorders. The results show that UMPA can accurately identify the neural unit modules of both healthy and diseased brain networks. UMPA has also been used to analyze brain networks in different states, such as the resting-state and task-state brain networks of healthy individuals. The results demonstrate that UMPA can accurately identify the unit modules of brain networks in different states. In conclusion, UMPA is a promising algorithm for the accurate and efficient identification of neural unit modules in brain networks, which has potential applications in disease detection and targeted therapy.

关键词： marine predators algorithm uniform design brain network neural unit module marine predators algorithm uniform design brain network neural unit module

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Adaptive crossover-based marine predators algorithm for global optimization problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第4期11卷 124-150页

作者： Yasear, Shaymah Akram Al Qasim Green Univ Comp Ctr Al Qasim 51013 Babil Iraq

The marine predators algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean's predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators - i.e., Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability. Graphical Abstract

关键词： adaptive sampling maximin distance criterion metaheuristic optimization engineering problem marine predators algorithm

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Image segmentation using multilevel thresholding based on type II fuzzy entropy and marine predators algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第13期80卷 19335-19359页

作者： Mahajan, Shubham Mittal, Nitin Pandit, Amit Kant Shri Mata Vaishno Devi Univ Sch Elect & Commun Engn Katra 182320 J&K India Chandigarh Univ Dept Elect & Commun Engn Mohali 140413 Punjab India

The digital image segmentation is an open problem that is growing day by day and is attracting the attention of researchers from last few years. Image resolution and their speed has led to the use of thresholding approaches. Image thresholding is simple, easy and effective method for image segmentation. Multi-level image thresholding is a key perspective in several real-time pattern recognition and image processing-based applications. It identifies pixels quickly and effectively in different groups indicating multiple regions in an image. Segmentation of images based on thresholding by using various intelligent optimization techniques with fuzzy entropy is widely utilized for defining thresholds in a better way to use them precisely. In this research, a novel technique for multi-level thresholding is proposed by combining Fuzzy Entropy Type II (FE-TII) with recently developed meta-heuristics named marine predators algorithm (MPA). For achieving optimal thresholds of an image, the maximization of entropy is tedious and consumes a lot of time with an increasing number of thresholds. The MPA method presented is analyzed in context with image segmentation, particularly on thresholds with TII-FE. For this reason, proposed methodology is evaluated using several images along with the distribution of histograms. For analyzing the performance efficiency of the proposed methodology, the results are compared and robustness is tested with efficiency of proposed technique to multi-level image segmentation, several images are used randomly from datasets.

关键词： Multilevel thresholding Digital image Type II fuzzy entropy Stochastic optimization marine predators algorithm Evolutionary computation

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