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Evolving marine predators algorithm by dynamic foraging strategy for real-world engineering optimization problems

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期123卷

作者： Shen, Baohua Khishe, Mohammad Mirjalili, Seyedali Hangzhou Dianzi Univ Informat Engn Coll Sch Management Hangzhou 311035 Zhejiang Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisban Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

The marine predators algorithm (MPA) is a novel hunting-based optimizer. The MPA's central concept is based on the well-known Levy Flight (LF) and Brownian Motion (BM) strategies as well as a simple transition model between these two strategies. The canonical MPA proposes three static steps to tune the transition behavior between the LF and BM strategies. Although MPA provides exemplary performance in many test functions, the discrete transition between the two mentioned phases causes it to get stuck in local optima when faced with real-world optimization problems. In order to address this shortcoming, this paper proposes a soft dynamic transition between LF and BM to model this encounter naturally, considering the continuous nature of the transition between LF and BM in marine predators' real life. In order to evaluate the performance of the developed Dynamic Foraging Strategy MPA (DFSMPA), twenty-nine optimization test functions, thirty complex CEC-BC-2017 functions, ten benchmarks of CEC06-2019 test suit, and ten real applicable engineering problems, including power system design, synthesis and process design, industrial chemical producer, power -electronic design, mechanical design, and animal feed ratio, are employed. The DFSMPA is evaluated against four groups of standard optimization approaches, including (1) Arithmetic Optimization algorithm (AOA), Slime Mould algorithm (SMA), Equilibrium Optimizer (EO), Niching Chimp Optimization algorithm (ChOA), Henry Gas Solubility Optimization (HGSO) as recent optimization algorithms, (2) Levy Flight GWO (LGWO) and Evolutionary algorithms with Adaptive Levy Mutations (EALM) as the two best dynamic Levy-based optimization algorithms, (3) SHADE, CMA-ES, and LSHADESPACMA as the three state-of-the-art optimization algorithms, and jDE100, DISHchain1e+12, CIPDE, and EBOwithCMAR as best performing algorithms in IEEE CEC06-2019 competition. Three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni- Dun

关键词： Optimization marine predators algorithm L?vy Flight Brownian Motion Dynamic foraging strategies

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An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation

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KNOWLEDGE-BASED SYSTEMS 2021年 229卷 107348-107348页

作者： Houssein, Essam H. Hussain, Kashif Abualigah, Laith Abd Elaziz, Mohamed Alomoush, Waleed Dhiman, Gaurav Djenouri, Youcef Cuevas, Erik Minia Univ Fac Comp & Informat Al Minya Egypt Bahria Univ Dept Comp Sci Karachi Pakistan Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zagazig Univ Fac Sci Zagazig Egypt Skyline Univ Coll Sch Informat Technol POB 1797 Sharjah U Arab Emirates Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India SINTEF Digital Oslo Norway Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara Jalisco Mexico

A recent meta-heuristic algorithm called marine predators algorithm (MPA) is enhanced using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency and convergence. A comprehensive set of experiments are performed to evaluate the MPA-OBL and prove the impact influence of merging OBL strategy with the original MPA in enhancing the quality of the solutions and the acceleration of the convergence speed, using IEEE CEC'2020 benchmark problems as recently complex optimization benchmark. In order to evaluate the performance of the proposed MPA-OBL, the effectiveness of conjunction of OBL with the original MPA and the other counterparts are calculated and compared with LSHADE with semi-parameter adaptation hybrid with CMA-ES (LSHADE_SPACMA-OBL), Restart covariance matrix adaptation ES (CMA_ES-OBL), Differential evolution (DE-OBL), Harris hawk optimization (HHO-OBL), Sine cosine algorithm (SCA-OBL), Salp swarm algorithm (SSA-OBL), and the original MPA. The extensive results and comparisons in terms of optimization metrics have revealed the superiority of the proposed MPA-OBL in solving the IEEE CEC'2020 benchmark problems and improving the convergence speed. Moreover, as a sequel to the proposed MPA-OBL, also, we have conducted experiments using two objective functions of Otsu and Kapur's methods over a variety of benchmark images at different level of thresholds based on three commonly evaluation matrices namely Peak signal-to-noise ratio (PSNR), Structural similarity (SSIM), and Feature similarity (FSIM) indices are analyzed qualitatively and quantitatively. Eventually, the statistical post-hoc analysis reveal that the MPA-OBL obtains highly efficient and reliable results in comparison with the other competitor algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Meta-heuristics marine predators algorithm Opposition-based learning Image segmentation Kapur's entropy and Otsu method Multilevel thresholding

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Parameter estimation of photovoltaic models using an improved marine predators algorithm

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ENERGY CONVERSION AND MANAGEMENT 2021年 227卷 113491-113491页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa Chakrabortty, Ripon K. Ryan, Michael Zagazig Univ Fac Comp & Informat Zagazig 44519 Sharqiyah Egypt UNSW Canberra Capabil Syst Ctr Sch Engn & IT Canberra ACT Australia

The abundance of solar energy as one of the clean energy forms offers a great advantage as an alternative to nonrenewable energy sources. The photovoltaic system is a promising technology that directly converts sunlight into a direct current. Parameter estimation of photovoltaic systems is a challenging task that has a significant influence on the efficiency of these systems. Most of the existing methods employed for identifying parameters of photovoltaic systems suffer from high computing burdens, fall into local optima, or struggle with the intricate adjustment required of the algorithm parameters to provide the best performance. This paper, therefore, proposes an improved algorithm based on the new metaheuristic marine predators algorithm to extract the optimal values of photovoltaic parameters. The improved marine predators algorithm employs a population improvement strategy to enhance the quality of the solutions by utilizing two different ways to handle the solutions inside the population-based on the population mean fitness. The location of a high-quality solution is improved using an adaptive mutation operation, while the location of a low-quality solution is updated according to the location of the best-obtained solution and the location of a good solution selected from the population. A good solution is chosen from the first half of the population after sorting its solutions in ascending order. The results of several experiments show the superior performance of the proposed algorithm compared to existing algorithms on a range of photovoltaic models. The results show that the proposed algorithm is highly correlated with the measured current-voltage data so that it can offer a useful alternative for parameter estimation of photovoltaic models.

关键词： Parameter estimation Photovoltaic model marine predators algorithm Solar energy

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Enhancing parameter identification and state of charge estimation of Li-ion batteries in electric vehicles using an improved marine predators algorithm

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JOURNAL OF ENERGY STORAGE 2024年 84卷

作者： Shaheen, Abdullah M. Hamida, M. A. Alassaf, Abdullah Alsaleh, Ibrahim Suez Univ Fac Engn Dept Elect Engn POB 43221 Suez Egypt Ecole Cent Nantes UMR CNRS LS2N 6004 Nantes France Univ Hail Coll Engn Dept Elect Engn Hail 55211 Saudi Arabia

The challenge of accurately determining the unknown parameters of Lithium -ion batteries (LiBs) is primarily attributed to the intricate dynamics of these batteries, their non-linear characteristics, and the absence of straightforward measurement methodologies. This necessitates the deployment of advanced estimation methodologies. Among these, state -of -charge (SoC) estimation algorithms are particularly critical, offering a viable solution to the complexities inherent in this identification problem. This paper proposes an Improved marine predators Technique (IMPT) as a methodological approach for elucidating the unidentified attributes of LiBs. This technique represents a novel advancement in the field, aiming to provide a more effective means of parameter determination for these complex energy storage devices. It is influenced by how marine predators forage in the ocean habitat, where they demonstrate flexibility and effective hunting techniques. It simulates the hunting process by iteratively increasing the fitness of candidate solutions through predation, scouting, and communication mechanisms. This is done by combining individual search with group collaboration to optimize solutions. By accounting for the potential for variation in the climate and environmental variables, the proposed IMPT supports the predator's tactics. The objective function and standard deviation error on the LiB dynamic model are validated by the developed IMPT. Its results are also compared with several novel optimizers. Experiments on the 40 Ah Kokam LiBs and the ARTEMIS driving cycle pattern are paired with simulation studies. The statistical results show how effective the suggested IMPT is as an identification method. Moreover, the proposed IMPT demonstrates significant precision in comparison to other existing optimization methods for the Kokam LiBs and the ARTEMIS drive cycle pattern.

关键词： Dynamic model Li -ion batteries marine predators algorithm Non-linear model Open circuit voltage relationship SoC estimation

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Quantum marine predators algorithm for addressing multilevel image segmentation

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APPLIED SOFT COMPUTING 2021年 110卷 107598-107598页

作者： Abd Elaziz, Mohamed Mohammadi, Davood Oliva, Diego Salimifard, Khodakaram Zagazig Univ Fac Sci Dept Math Zagazig Egypt Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia Persian Gulf Univ CIIORG Bushehr 75168 Iran Univ Guadalajara Dept Ciencias Computac CUCEI AvRevoluc 1500 Guadalajara Jalisco Mexico

This paper proposes a modified marine predators algorithm based on quantum theory to handle the multilevel image segmentation problem. The main aims of using quantum theory is to enhance the ability of marine predators algorithm to find the optimal threshold levels to enhance the segmentation process. The proposed quantum marine predators algorithm gets the idea of finding a particle in the space based on a possible function borrowed from the Schrodinger wave function that determines the position of each particle at any time. This rectification in the search mechanism of the marine predators algorithm contributes to strengthening of exploration and exploitation of the algorithm. To analyze the performance of the proposed algorithm, we conduct a set of experiments. In the first experiment, the results of the developed quantum marine predators algorithm are compared with eight well-known meta-heuristics based on benchmark test functions. The second experiment demonstrates the applicability of the algorithm, in addressing multilevel threshold image segmentation. A set of ten gray-scale images assess the quality of the quantum marine predators algorithm and its performance is compared with other meta-heuristic algorithms. The experimental results show that the proposed algorithm performs well compared with other algorithms in terms of convergence and the quality of segmentation. (C) 2021 Elsevier B.V. All rights reserved.

关键词： marine predators algorithm Quantum theory Swarm intelligence Image segmentation Global optimization

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An improved marine predators algorithm for the optimal design of hybrid renewable energy systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104722-104722页

作者： Houssein, Essam H. Ibrahim, Ibrahim E. Kharrich, Mohammed Kamel, Salah Minia Univ Fac Comp & Informat Al Minya Egypt Luxor Univ Fac Comp & Informat Luxor Egypt Mohammed V Univ Mohammadia Sch Engineers Elect Engn Dept Ibn Sina St PB 765 Rabat 10090 Morocco Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt

Microgrid technologies are exciting energy sources that are economically feasible for current and future applications in light of increased energy demand and the depletion of traditional sources. This article focuses on the latest metaheuristic algorithm, the marine predators algorithm (MPA), in the field of energy. To investigate a method for reducing the system's investment costs in Minia, Egypt, the combination of reinforcement learning (RL) with MPA is used to build a new method, Deep-MPA, where RL principles are applied to adjust and enhance the lack of MPA in global searching. The exploration/exploitation ratio is regulated by varying the step size, which affects the efficiency of the MPA. Instead of updating the value of the parameter for all agents in the same manner, RL principles are used to update it on the basis of the current individual state. Additionally, to resolve the common challenge of using RL to determine the appropriate global search parameters for MPA, Deep-MPA is used to design a hybrid renewable energy microgrid system, which includes photovoltaic panels, a wind turbine system, a diesel generator, and battery storage systems. These are some of the criteria and constraints that this system may require to ensure its stability, robustness, performance, and load satisfaction. The proposed Deep-MPA is verified by contrasting the results with different algorithms in the CEC'2017 test. Moreover, Wilcoxon's test validates the statistical significance of the Deep-MPA. The energy cost is reduced by 6% of total consumption.

关键词： marine predators algorithm Artificial neural network Hybrid renewable energy system Hybrid microgrid Design Adaptive exploration rate

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Optimal coordination of directional overcurrent relays in complex networks using the Elite marine predators algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期221卷

作者： Merabet, Oussama Bouchahdane, Mohamed Belmadani, Hamza Kheldoun, Aissa Eltom, Ahmed Univ Mhamed Bougara Inst Elect & Elect Engn Lab Signals & Syst Boumerdes Algeria Blida 1 Univ Fac Technol Elect Dept SET Lab Blida Algeria Univ Tennessee Chattanooga TN 37403 USA

The integration of renewable energy sources in the distribution network (DN) has had a significant influence by reducing power loss and enhancing network dependability. Aside from that, the protection system has met coordination issues as a result of bidirectional power flow and variations in fault levels. Therefore, an optimal coordination strategy is required to deal with relays coordination problem. The coordination problem of the directional overcurrent relays (DOCRs) is a restricted optimization issue that involves determining appropriate time dial settings (TDS) and plug setting (PS) to reduce relays operating time. Currently, a various nontraditional optimization strategies have been presented to overcome this challenge. In this paper, a modified version of the marine predators algorithm (MPA) referred to as Elite marine predator (EMPA) is developed for the optimal coordination of DOCRs. Therefore, the EMPA method is used to find out the optimal settings for the DOCRs problem. The suggested algorithm's performance is evaluated using standard test systems, including 3-bus, 8bus, 9-bus, and 15-bus. The findings are compared with the traditional MPA and with other recent optimization methods presented in the literature to prove the efficiency and superiority of the proposed EMPA in reducing relay operation time for optimal DOCRs coordination.

关键词： Protection system Directional overcurrent relay Optimal coordination Relay setting marine predators algorithm

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A modified marine predators algorithm for solving single- and multi-objective combined economic emission dispatch problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 164卷

作者： Hassan, Mohamed H. Yousri, Dalia Kamel, Salah Rahmann, Claudia Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Minist Elect & Renewable Energy Cairo Egypt Fayoum Univ Fac Engn Engn Dept Al Fayyum Egypt Univ Chile Dept Elect Engn Santiago Chile

Decreasing the emission of greenhouse gases and the fuel cost are the main principles of the combined economic emission dispatch (CEED) optimization problem. In this work, a modified version of the marine predators algorithm (MMPA), is proposed and applied for solving single- and bi-objective CEED problems. MMPA is suggested to improve the performance of conventional MPA. It includes a comprehensive learning approach to share the best experiences among all the individuals to avoid premature convergence. The MMPA increases the populations' effectiveness to reach the optimum fitness solution and this is helpful in cases of single- and bi-objective functions. The proposed MMPA has been mathematically assessed with twenty-eight 50-dimensional benchmarks of CEC2017. Also, it is successfully tested on four electrical power generation systems (each of them with 3, 5, 6, and 26 generating units respectively). The results achieved by MMPA are compared with those obtained by the original MPA and other recent optimization methods. Concerning the bi-objective problem, the Pareto approach is combined with the proposed MMPA and conventional MPA techniques to reach a group of non-dominated (ND) solutions, and then the fuzzy method is utilized to choose the best compromise solution. The reached results display that the proposed MMPA technique gives better performance than the conventional MPA and the others.

关键词： Greenhouse gases marine predators algorithm Price penalty factor Pareto front Combined economic and emission dispatch

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Enhanced marine predators algorithm with Local Escaping Operator for Global Optimization

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KNOWLEDGE-BASED SYSTEMS 2021年 232卷 1页

作者： Oszust, Mariusz Rzeszow Univ Technol Dept Comp & Control Engn Wincentego Pola 2 PL-35959 Rzeszow Poland

The recently introduced marine predators algorithm (MPA) exhibits competitive performance in solving optimization problems. However, it often prematurely converges due to an imbalance between its exploration and exploitation capabilities. Therefore, in this paper, an improved MPA variant using a proposed Local Escaping Operator (LEO) is introduced. In the MPA, solution candidates are replaced by better candidates from the previous iteration, mimicking a memory of already visited prey abundant areas. This indicates a weak inter-dependence between the candidates in the population and possible acceptance of new solution candidates created outside the algorithm without damaging the optimization process. Consequently, in the proposed approach, the worst candidates are replaced with solutions created by the LEO. The LEO is based on representative solutions, taking into account the relationship between predators and the characteristics of their population. The approach is experimentally compared with the state-of-the-art meta-heuristics on 82 test functions, including IEEE Congress on Evolutionary Computation test suites (CEC'14 and CEC'17) and three engineering problems. The results show the superiority of the LEO-MPA over the MPA and recent algorithms. Furthermore, in this paper, the suitability of the hybridization of meta-heuristics with the LEO is discussed and successful attempts with the best algorithms are shown. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Global optimization Meta-heuristics Hybridization marine predators algorithm Representative solutions Local escaping operator

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An automatic arrhythmia classification model based on improved marine predators algorithm and Convolutions Neural Networks

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期187卷 115936-115936页

作者： Houssein, Essam H. Hassaballah, M. Ibrahim, Ibrahim E. AbdElminaam, Diaa Salama Wazery, Yaser M. Minia Univ Fac Comp & Informat Al Minya Egypt South Valley Univ Fac Comp & Informat Dept Comp Sci Qena Egypt Luxor Univ Fac Comp & Informat Luxor Egypt Benha Univ Fac Comp & Artificial Intelligence Banha Egypt Misr Int Univ Fac Computers Sci Misr Egypt

Preparation of Convolutional Neural Networks (CNNs) for classification purposes depends heavily on the knowledge of hyper-parameters tuning. This study aims, in particular in task of automated electrocardiograms (ECG), to minimize the user variability in the CNN training by searching and optimizing the CNN parameters automatically. In the clinical practice, the task of ECG classification analysis is restricted by existing models' configuration. The hyper-parameters of the CNN model should be adjusted for the ECG classification problem. The best configuration for hyper-parameters is selected to have an impact on the production of the model. Deep knowledge of deep learning algorithms and suitable optimization techniques are also needed. Although there are many strategies for automated optimization, different benefits and disadvantages occur as they are applied to ECG classification problem. Here we present a CNN model for classification of non-ectopic (N), ventricular ectopic (V), supraventricular ectopic (S), and fusion (F) ECG rhythmias by the hybrid models based on modified version of marine predators algorithm (MPA) and CNN, known as the IMPA-CNN. The proposed model summarizes the feature extraction techniques of major features and, thus, outperforms other current classification models through automatically select the best hyper-parameters configuration of the CNN model. To reduce the time and complication complexity, optimum characteristics have been extracted directly from the raw signal using 1D-local binary pattern, higher order statistics, central moment, Hermite basis function discrete wavelet transform, and R-R intervals. Then, a modified version of MPA algorithm is used to select appropriate hyper-parameters for the CNN model like initial learning rate for the CNN model that is one of the major hyper parameters effect output performance, optimizer type which can be set to stochastic gradient descent (SGD), adaptive moment estimation (Adam), root mean squar

关键词： Electrocardiogram (ECG) Arrhythmia classification Metaheuristics marine predators algorithm Convolution Neural Network Features selection

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