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A Novel Approach Based on marine predators algorithm for Medical Image Enhancement

SENSING AND IMAGING 2023年第1期24卷 6-6页

作者： Dinh, Phu-Hung Thuyloi Univ Fac Comp Sci & Engn 175 Tay Son Hanoi Vietnam

One of the approaches to improve the performance of image processing applications is to enhance the input image quality. Some common problems with medical images include lack of sharpness, noise, or low contrast. In this study, we propose a new algorithm to improve the quality of brain Magnetic resonance images (MRI). Firstly, we use image enhancement algorithms such as Contrast limited adaptive histogram equalization (CLAHE), Denoise based on convolutional neural network (DN-CNN), and Laplacian edge detection (LED) to create I-1 , I-2 , and I-3 images from the input image (I), respectively. Secondly, the marine predators algorithm (MPA) is used to find the adaptive parameters beta(1) , beta(2) , and beta(3) corresponding to I-1 , I-2 , and I-3. Finally, the enhanced image is made up of the sum of the I-1 , I-2 , and I-3 images multiplied by the coefficients beta(1) , beta(2) , and beta(3) , respectively. Experiments show that the images produced by our model are better than those produced by some recent image enhancement methods. Furthermore, our model also allows for improving the performance of image fusion algorithms.

关键词： Image enhancement Contrast limited adaptive histogram equalization Laplace edge detection marine predators algorithm

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Optimization of biodegradation yield of reactive blue 49: An integrated approach using response surface methodology based marine predators algorithm

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JOURNAL OF MICROBIOLOGICAL METHODS 2023年第1期206卷 106691-106691页

作者： Oge, Ezgi Yaman, Belma Nural Sahin, Yeliz Buruk Eskisehir Osmangazi Univ Grad Sch Nat & Appl Sci Dept Ind Engn Eskisehir Turkiye Eskisehir Osmangazi Univ Fac Engn & Architechture Dept Biomed Engn Eskisehir Turkiye Eskisehir Osmangazi Univ Fac Engn & Architechture Dept Ind Engn Eskisehir Turkiye

Biodegradation involving the use of biological systems are proving to be more cost-effective, energy efficient and environmentally friendly method for treatment with dye. There are also some advantages of enzymatic degra-dation to remove pollutants from textile effluents. They are specific and selectable for the substrates. Moreover, the usage of enzymatic degradation with laccase has environmental and social dimensions of sustainable bio-economy when compared with commercial solutions. In this study, besides the conventional RSM (Response Surface Methodology) approaches, the performance of a new metaheuristic, MPA (marine predators algorithm) integrated with RSM has also been tested. The effect of four independent variables time, enzyme amount, agitation speed, and initial dye concentration have been studied to obtain maximum decolorization. Two major techniques of RSM, namely Box Behnken Design (BBD) and Central Composite Design (CCD) have been used to determine optimal levels of parameters. The highest decolorization efficiency (%) of Reactive Blue 49 (RB49) has been achieved as 92.82% with BBD and 90.56% with CCD. Maximum efficiency for BBD based MPA as being 94.69% has been obtained at 70.96 min, 1.5 mL, 74.63 rpm, and 99.09 ppm. For CCD based MPA, the level of efficiency has been obtained as 93.1% at 39.69 min, 1 mL, 72.07 rpm, and 74.41 ppm. The results of MPA indicate that using metaheuristics combined with RSM is a suitable and sustainable way to optimize parameters of RB49% for decolorization.

关键词： Decolorization Desirability Laccase marine predators algorithm Response surface methodology

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Optimized cascade fractional-order 3DOF-controller for frequency regulation of a hybrid power system using marine predators algorithm

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2023年第5期36卷

作者： Patel, Vivek Guha, Dipayan Purwar, Shubhi Motilal Nehru Natl Inst Technol Allahabad Elect Engn Dept Prayagraj India

This article investigates the competence of a cascade fractional-order three-degree-of-freedom (CC-FO-3DOF) controller for power-frequency regulation of a hybrid wind-diesel (hyWD) power system subjected to continuous load variation and unknown/uncertain wind power perturbation. The proposed cascade controller utilizes fractional-order 3DOF-PID and tilt-integral-derivative controllers as slave and master controllers, respectively, to ensure fast disturbance rejection and high damping of power-frequency oscillations. A disturbance observer is designed to estimate system states and unknown/uncertain wind velocity. Subsequently, an improved control law is designed by augmenting the disturbance estimation, which ensures a higher degree of robustness of the closed-loop system against disturbances. marine predators algorithm (MPA) is applied to search near-optimum settings of the proposed controller, exercising an integral error-based objective function. The effectiveness of the proposed controller is demonstrated by comparing results with the outputs of other prevalent controllers reported in the state-of-art. Extensive analysis of the presented results establishes the superiority of the MPA-tuned CC-FO-3DOF controller in terms of maximum peak overshoot, frequency nadir, speed of response, and steady-state accuracy. Finally, the robust closed-loop stability of the investigated hyWD system has been affirmed applying Kharitonov's stability method, considering dynamic variation in system parameters.

关键词： cascade fractional-order controller disturbance observer hybrid wind-diesel system marine predators algorithm robust stability three-degree-of-freedom

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A modified marine predators optimization algorithm for simultaneous network reconfiguration and distributed generator allocation in distribution systems under different loading conditions

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ENGINEERING OPTIMIZATION 2022年第4期54卷 687-708页

作者： Shaheen, Abdullah M. Elsayed, Abdallah M. El-Sehiemy, Ragab A. Kamel, Salah Ghoneim, Sherif S. M. Suez Univ Fac Engn Elect Engn Dept Suez Egypt Damietta Univ Fac Engn Elect Engn Dept Dumyat Egypt Kafrelsheikh Univ Elect Engn Dept Fac Engn Kafrelsheikh Egypt Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt Taif Univ Coll Engn Dept Elect Engn At Taif Saudi Arabia

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator's strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine-cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation.

关键词： marine predators algorithm distribution systems voltage stability power losses distributed generators reconfiguration

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A novel hybrid algorithm based on improved marine predators algorithm and equilibrium optimizer for parameter extraction of solar photovoltaic models

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Heliyon 2024年第19期10卷 e38412页

作者： Liang, Ziyuan Wang, Zhenlei Mohamed, Ali Wagdy Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity;an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation;the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed;and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field. © 2024

关键词： Equilibrium optimizer marine predators algorithm Parameter extraction Photovoltaic models Solar cell

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A Noise Reduction algorithm for White Noise and Periodic Narrowband Interference Noise in Partial Discharge Signals

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APPLIED SCIENCES-BASEL 2025年第4期15卷 1760-1760页

作者： Cao, Jiyuan Wang, Yanwen Zhu, Weixiong Zhang, Yihe China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China

Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a PD noise reduction algorithm based on improved singular value decomposition (SVD) and multivariate variational mode decomposition (MVMD) for white Gaussian noise (WGN) and periodic narrowband interference signal noise. The specific noise reduction algorithm is divided into three noise reduction processes: The first noise reduction completes the suppression of narrowband interference in the noisy PD signal by the SVD algorithm with the guidance signal. The guidance signal is composed of a sinusoidal signal of the accurately estimated narrowband interference frequency component, and the amplitude is twice the maximum amplitude of the noisy PD signal. The second noise reduction decomposes the noisy PD signal after filtering the narrowband interference signal into k optimal intrinsic mode function by the MVMD after parameter optimization. By calculating the kurtosis value of each intrinsic mode function, it is determined whether it is the PD dominant component or the noise dominant component, and the noise dominant component is subjected to 3 sigma filtering to obtain the reconstructed PD signal. The third noise reduction uses a new wavelet threshold algorithm to denoise the reconstructed PD signal to obtain the denoised PD signal. The overall noise reduction algorithm proposed in this paper is compared with some existing methods. The results show that this method has a good effect on reducing the noise of PD signals measured in simulation and field.

关键词： partial discharge noise reduction multivariate variational mode decomposition marine predators algorithm singular value decomposition

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Efficient Technique for Joint Parameter Estimation of Mixed Far Field Targets and Near Field Jammers in Phased Array Radar

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IEEE ACCESS 2025年 13卷 55887-55898页

作者： Zaman, Fawad Akbar, Sadiq Akhtar, Zohaib Phanomchoeng, Gridsada Chulalongkorn Univ Fac Engn Dept Mech Engn Bangkok 10330 Thailand COMSATS Univ Islamabad Dept Elect Engn Islamabad 45550 Pakistan Imperial Coll London Dept Elect & Elect Engn London SW7 2BX England Chulalongkorn Univ Ctr Excellence Appl Med Virol Bangkok 10330 Thailand

Parameter estimation of the impinging signal in the sensor arrays is one of the challenging issues in Phased Array Radar (PAR). Most often, a PAR operates in the presence of jammers deployed in its near field. In order to mitigate the jammers signals, it is important that PAR must know the exact Direction Of Arrival (DOA) and ranges of the jammers along with the DOAs of the actual targets located in the far field. This information is crucial for the PAR to steer the mainbeam and nulls in the desired and undesired (jammers) directions, respectively. In this work, we consider a PAR that simultaneously receives the desired plane waves and the undesired spherical waves from the far field targets and near field jammers respectively. The plane waves are the function of the unknown DOA, whereas the spherical waves depend on the DOAs and ranges of the jammers. A novel approach based on the Gradient Based Optimizer (GBO) algorithm and marine predators algorithm (MPA) is proposed to estimate jointly the unknown parameters related to the far field targets and near field jammers. The GBO uses two operators called Gradient Search Rule (GSR) and Local Escaping Operator (LEO). The GSR enhances the convergence rate and improves the exploration tendency to obtain the better solution in the search space, while the LEO helps the algorithm avoid getting stuck in the local optima. The MPA uses different velocities ratios based on predators and prey movement in the ocean to achieve better exploration and exploitation in the search domain. A cost function is developed and optimized using GBO and MPA, which is based on the Penalty Function (PF) and Mean Square Errors (MSE) of the system. The PF controls the deviation in the MSE from the desired result during the optimization process. The proposed schemes are not only compared with each other but also with the state-of-the-art MUSIC algorithm. Extensive Monte Carlo simulations are enacted for diverse scenarios to justify the legitimacy of t

关键词： Jamming Direction-of-arrival estimation Phased arrays Radar Sensor arrays Radar antennas Cost function Vectors Multiple signal classification Estimation Antenna arrays direction of arrival gradient based optimization algorithm marine predators algorithm phased array radar plane waves spherical waves

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Chaotic quasi-opposition marine predator algorithm for automatic data clustering

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-64页

作者： Ouertani, Mohamed Wajdi Manita, Ghaith Chhabra, Amit Korbaa, Ouajdi Univ Manouba ENSI Manouba Tunisia Univ Manouba ESEN Manouba Tunisia Guru Nanak Dev Univ Dept Comp Engn & Technol Amritsar India Univ Sousse ISITCom Sousse Tunisia

This paper presents a novel approach to data clustering by introducing the Chaotic Quasi-Opposition-Based Learning marine predators algorithm (CQOBLMPA). The primary objective is to enhance the efficiency and accuracy of automatic data clustering by integrating chaotic local search and quasi-opposition-based learning (QOBL) into the marine predators algorithm (MPA). The proposed CQOBLMPA method addresses the slow convergence issue of MPA and improves its ability to avoid local optima. Our contributions include: firstly, an enhanced exploration phase through QOBL to accelerate convergence;secondly, the refinement of the search process using chaotic local search techniques;and thirdly, the development of an automatic clustering framework that does not require prior knowledge of the number of clusters. The effectiveness of CQOBLMPA is demonstrated through extensive tests on 23 global benchmark functions and the CEC2022 benchmarks, showing significant improvements over the original MPA and other metaheuristics. Additionally, the proposed method is validated on eleven real-world and four artificial datasets, achieving superior clustering performance as measured by the Davies-Bouldin index (DB-index) and the Compact-Separated index (CS-index). The results confirm the robustness and efficiency of CQOBLMPA, highlighting its potential for solving complex clustering problems in various domains, with notable performance in the CEC2022 benchmark tests.

关键词： marine predators algorithm Metaheuristics Quasi opposition-based learning Chaotic local search Automatic clustering Function optimization

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State observer for semi-active suspension system based on MPA-UKF

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年

作者： Yang, Lintao Zhao, Yuzhuang Ren, Hongbin Chen, Jingwei Beijing Inst Technol Sch Mech Engn Beijing Peoples R China

With consideration of the nonlinearity of semi-active suspensions and to enhance the accuracy of nonlinear state observers while achieving the automatic tuning of parameters, the marine predators algorithm (MPA) is applied to the noise covariance tuning process of the Unscented Kalman Filter (UKF) based on a seven-degree-of-freedom nonlinear vehicle model. Therefore, a novel MPA-UKF algorithm is proposed for the state observation of semi-active suspensions. Additionally, a semi-active suspension vehicle test platform is set up to validate the algorithm's performance under various driving conditions. The experimental outcomes demonstrate that the proposed algorithm improves parameter tuning efficiency while enhancing the accuracy of the Unscented Kalman Filter by 10% and can serve as the observer for suspension controllers.

关键词： Semi-active suspension state observer unscented Kalman filter marine predators algorithm

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A crossover-integrated marine Predator algorithm for feature selection in intrusion detection systems within IoT environments

INTERNET OF THINGS

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INTERNET OF THINGS 2025年 31卷

作者： Makhadmeh, Sharif Naser Fraihat, Salam Awad, Mohammed Sanjalawe, Yousef Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Univ Jordan King AbdullahII Sch Informat Technol Dept Informat Technol Amman 11942 Jordan Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Amer Univ Ras Al Khaimah Dept Comp Sci & Engn POB 72603 Ras Al Khaymah U Arab Emirates Al Aqsa Univ Dept Comp Sci POB 4051 Gaza 4051 Palestine AL Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan

In recent times, there has been a significant rise in cyberattacks targeting the Internet of Things (IoT) and cyberspace in general. Detecting intrusions in a time series environment is a critical challenge for Network Intrusion Detection Systems (NIDS). Building an effective NIDS requires carefully establishing an efficient model, with machine learning (ML) playing a prominent role. The performance of ML models depends on selecting the most informative feature subset. Recently, metaheuristic (MH) optimization methods have been effective in identifying these key features. However, standard MH methods require adjustment to incorporate NIDS-specific knowledge for optimal results, improving both MH performance and ML accuracy. This paper introduces a novel NIDS framework based on three key phases: preprocessing, optimization, and generalization. In the preprocessing phase, several datasets undergo cleaning and under- sampling. In the optimization phase, an enhanced version of the marine predators algorithm (MPA) is proposed, utilizing the crossover operator to identify the most relevant features. The proposed method is called MPAC. The crossover operator is utilized to boost the exploitation capabilities of the MPA and find the optimal local solution for the NIDS. Finally, the selected features are applied to the NIDS. Eight different datasets are employed for examination and evaluation using different evaluation measurements to assess the effectiveness of the proposed NIDS. The experimental evaluation is organized into three phases: evaluating the proposed crossover modification by applying it to five algorithms and comparing results to the originals, comparing the results of the proposed algorithms to prove the robust performance of the MPAC, and comparing the results obtained by the MPAC with the stat-of-the-arts. The proposed MPAC confirmed its demonstration and high performance in detecting network attacks, wherein in the first evaluation phase, the proposed appro

关键词： Network intrusion detection system marine predators algorithm Crossover Optimization Internet of Things

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