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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115665-115665页

作者： Jia, Heming Peng, Xiaoxu Lang, Chunbo Sanming Univ Dept Informat Engn Sanming 365004 Fujian Peoples R China Harbin Inst Technol Sch Astronaut Harbin 150040 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

In this paper, remora optimization algorithm (ROA) is proposed, which is a new bionics-based, natural-inspired, and meta-heuristic algorithm. The inspiration for ROA is mainly due to the parasitic behavior of remora. Different locations are updated in different hosts: In some large hosts, remora feeds on the host's ectoparasites or wreckage and evades natural enemies, for example in the case of giant whales. In some small hosts, remora follows the host to move to the bait-rich area to prey, taking the fast-moving swordfish as an example. In the case of these two update methods, remora also makes some judges based on experience. If it takes the initiative to prey, it updates the host, makes a global update. If it eat around the host, remora does not change the host, and continues to local update. This algorithm is more inclined to provide a new idea for memetic algorithm, because the host in ROA can be reasonably replaced, such as ships, turtles, etc. The above dynamic mode and behavior are simulated mathematically and the validity of the ROA is tested with 29 benchmark questions and 5 actual engineering questions. Parallel comparisons are made with 10 other natural heuristics. The statistical results and comparisons show that ROA provides a very promising prospect and a strong competitive ability compared to other state-ofthe-art heuristic techniques.

关键词： remora optimization algorithm Bionics-based Natural-inspired Meta-heuristic optimization

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Group-driven remora optimization algorithm with multiple search and regeneration strategies

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-17页

作者： Peng, Fei Zhong, Rui Zhang, Chao Yu, Jun Niigata Univ Grad Sch Sci & Technol Niigata Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Univ Toyama Fac Engn Toyama Japan Niigata Univ Inst Sci & Technol Niigata Japan

We have introduced group-driven strategies into the conventional remora optimization algorithm (ROA) and amalgamated regeneration strategies to further enhance the generalization ability. This proposed algorithm were termed the Group-Driven remora optimization algorithm (GROA). Firstly, the "Group dividing" divides the search individuals into different groups based on Euclidean distance, further subdividing individuals within each group with varying functionalities of search, using multiple search strategies. That provides different exploration scales and candidate solution diversity to the algorithm. Whereafter, if too many individuals are gathered together, "Random regeneration" randomly regenerates individuals. Finally, the offspring selection operation was produced for more efficient offspring evolution. To assess the effectiveness of the proposed GROA, the ablation experiments on the proposal and its derived variants, along with comparative experiments among GROA and the other five novelty and classical evolutionary algorithms (EAs) are conducted on CEC2017 test suits. Also, comparison experiments on five engineering design problems are implemented for appraising the performance in real-world optimization problems. Finally, we discuss the improvement and deficiency of the new algorithm, GROA. The experimental and statistical results demonstrate that our approach significantly improves the optimization accuracy and search efficiency of the conventional algorithm in most experimental problems.

关键词： Evolutionary computation remora optimization algorithm Group dividing strategy Single object optimization

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An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第4期19卷 3994-4037页

作者： Zheng, Rong Jia, Heming Abualigah, Laith Liu, Qingxin Wang, Shuang Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Sanming Univ Sch Educ & Mus Sanming 365004 Peoples R China

The remora optimization algorithm (ROA) is a newly proposed metaheuristic algorithm for solving global optimization problems. In ROA, each search agent searches new space according to the position of host, which makes the algorithm suffer from the drawbacks of slow convergence rate, poor solution accuracy, and local optima for some optimization problems. To tackle these problems, this study proposes an improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own. In AFM, each remora has a small chance to search food randomly or according to the current food position. Thus the AFM can effectively expand the search space and improve the accuracy of the solution. To substantiate the efficacy of the proposed IROA, twenty-three classical benchmark functions and ten latest CEC 2021 test functions with various types and dimensions were employed to test the performance of IROA. Compared with seven metaheuristic and six modified algorithms, the results of test functions show that the IROA has superior performance in solving these optimization

关键词： remora optimization algorithm arithmetic optimization algorithm metaheuristic algorithm swarm intelligence global optimization

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Multi-strategy remora optimization algorithm for solving multi-extremum problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第4期10卷 1315-1349页

作者： Jia, Heming Li, Yongchao Wu, Di Rao, Honghua Wen, Changsheng Abualigah, Laith Sanming Univ Sch Informat Engn Sanming City 365004 Peoples R China Sanming Univ Sch Educ & Mus Sanming 365004 Peoples R China Al Al Bayt Univ Prince Hussein Bin Abdullah Coll Informat Technol Mafraq City Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman City 11931 Jordan Univ Sains Malaysia Sch Comp Sci Pulau Pinang City 11800 Malaysia

A metaheuristic algorithm that simulates the foraging behavior of remora has been proposed in recent years, called ROA. ROA mainly simulates host parasitism and host switching in the foraging behavior of remora. However, in the experiment, it was found that there is still room for improvement in the performance of ROA. When dealing with complex optimization problems, ROA often falls into local optimal solutions, and there is also the problem of too-slow convergence. Inspired by the natural rule of "Survival of the fittest", this paper proposes a random restart strategy to improve the ability of ROA to jump out of the local optimal solution. Secondly, inspired by the foraging behavior of remora, this paper adds an information entropy evaluation strategy and visual perception strategy based on ROA. With the blessing of three strategies, a multi-strategy remora optimization algorithm (MSROA) is proposed. Through 23 benchmark functions and IEEE CEC2017 test functions, MSROA is comprehensively tested, and the experimental results show that MSROA has strong optimization capabilities. In order to further verify the application of MSROA in practice, this paper tests MSROA through five practical engineering problems, which proves that MSROA has strong competitiveness in solving practical optimization problems.

关键词： remora optimization algorithm random restart strategy information entropy visual perception multi-strategy optimization algorithm

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ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction Through Tongue Image Analysis Using remora optimization algorithm

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BIG DATA 2023年第6期11卷 452-465页

作者： Sreenivasu, S. V. N. Patra, P. Santosh Kumar Midasala, Vasujadevi Murthy, G. S. N. Janapati, Krishna Chaitanya Kumar, J. N. V. R. Swarup Kumar, Pala Mahesh Narasaraopeta Engn Coll A Dept Comp Sci & Engn Narasaraopet India St Martins Engn Coll A Dept Comp Sci & Engn Secunderabad India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vaddeswaram India Aditya Coll Engn Dept Comp Sci & Engn Surampalem India Vardhaman Coll Engn Dept Elect & Commun Engn Hyderabad India GITAM GITAM Sch Technol Dept Comp Sci & Engn Visakhapatnam India SAK Informat Dept Artificial Intelligence Hyderabad India

Tongue analysis plays the major role in disease type prediction and classification according to Indian ayurvedic medicine. Traditionally, there is a manual inspection of tongue image by the expert ayurvedic doctor to identify or predict the disease. However, this is time-consuming and even imprecise. Due to the advancements in recent machine learning models, several researchers addressed the disease prediction from tongue image analysis. However, they have failed to provide enough accuracy. In addition, multiclass disease classification with enhanced accuracy is still a challenging task. Therefore, this article focuses on the development of optimized deep q-neural network (DQNN) for disease identification and classification from tongue images, hereafter referred as ODQN-Net. Initially, the multiscale retinex approach is introduced for enhancing the quality of tongue images, which also acts as a noise removal technique. In addition, a local ternary pattern is used to extract the disease-specific and disease-dependent features based on color analysis. Then, the best features are extracted from the available features set using the natural inspired remora optimization algorithm with reduced computational time. Finally, the DQNN model is used to classify the type of diseases from these pretrained features. The obtained simulation performance on tongue imaging data set proved that the proposed ODQN-Net resulted in superior performance compared with state-of-the-art approaches with 99.17% of accuracy and 99.75% and 99.84% of F1-score and Mathew's correlation coefficient, respectively.

关键词： Indian ayurvedic medicine tongue image analysis local ternary pattern deep q-neural network remora optimization algorithm

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Synergizing remora optimization algorithm and Transfer Learning for Visual Places Recognition in Intelligent Transportation Systems and Consumer Electronics

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3731-3739页

作者： Ragab, Mahmoud Abukhodair, Felwa AL-Ghamdi, Abdullah AL-Malaise Shabanah, Sahar Alfasi, Baraa Alyamani, Mohannad King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Smart Environm Res Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Smart Environm Res Jeddah 21589 Saudi Arabia Dar Al Hekma Univ Sch Engn Comp & Design Informat Syst Dept Jeddah 22246 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Informat Syst Dept Jeddah 21589 Saudi Arabia Dar Al Hekma Univ Sch Engn Comp & Design Comp Sci Dept Jeddah 22246 Saudi Arabia Umm Al Qura Univ Coll Engn & Architecture Civil Engn Dept Mecca 24382 Saudi Arabia

Visual Place Recognition (VPR) sits at the junction of artificial intelligence (AI) and our physical world, promising to modernize both Consumer Electronics (CE) and Intelligent Transportation Systems (ITS). VPR authorizes self-driving cars to recognize landmarks, straight difficult connections, and realize traffic signs, making independent driving securer and more effective. VPR in ITS includes the application of computer vision (CV) models to study and understand visual information from the environment, permitting transports and set up to understand and direct different surroundings. This sophisticated technique includes the identification and understanding of landmarks, road signs, and other related visual cues, simplifying present decision-making for improved navigation, traffic management, and complete system intelligence. By connecting innovative image processing models and machine learning (ML) methods, visual place detection plays an essential part in enhancing protection, efficacy, and functionality within intelligent transportation methods. Therefore, this study designs a new remora optimization algorithm and Transfer Learning for Visual Places Recognition (ROATL-VPR) in the ITS environment. The ROATL-VPR technique aims to effectually and robustly detect the visual places using an optimal DL model. At the preliminary stage, the ROATL-VPR model mainly pre-processes input images using the Wiener filtering (WF) technique. For learning the complex and intrinsic patterns in the image, the ROATL-VPR technique follows the EfficientNetB3 model as feature extraction. Moreover, the hyperparameter tuning of the EfficientNetB3 method takes place by utilizing the ROA. To identify the visual places precisely, the ROATL-VPR technique applies Manhattan distance which detects the visual places proficiently. The performance validation of the ROATL-VPR method takes place by utilizing a benchmark VPR database. The experimental outcomes highlighted that the ROATL-VPR technique

关键词： Visual place recognition intelligent transportation systems computer vision transfer learning remora optimization algorithm

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A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 1期56卷 159-216页

作者： Liu, Qingxin Li, Ni Jia, Heming Qi, Qi Abualigah, Laith Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hainan Normal Univ Sch Math & Stat Haikou 571158 Peoples R China Hainan Normal Univ Key Lab Data Sci & Intelligence Educ Minist Educ Haikou 571158 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search methods are costly in determining optimal thresholds and the conventional remora optimization algorithm (ROA) is prone to the premature convergence. This paper presents a chimp-inspired remora optimization algorithm (HCROA) to search optimal threshold levels, and the cross-entropy is employed as the objective function. In HCROA, the particles' position are adjusted by the Chimp optimization algorithm (ChOA) because of its good exploitation ability and sufficient diversity. With this change, HCROA achieves both the intra-group diversity intelligence and a suitable balance between exploration and exploitation. To validate its performance, a series of experiments are performed. First, we test the HCROA's segmentation accuracy by a set of natural gray-scale images with different thresholds. Second, HCROA is implemented for noisy image segmentation to evaluate its robustness. Several reference-based measurements including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Feature Similarity (FSIM), Quality Index based on Local Variance (QILV), Haar wavelet-based Perceptual Similarity Index (HPSI), Wilcoxon test, and CPU time have been considered for evaluating the proposed method. Additionally, eight well-known predecessors are injected for parallel comparison. The comparison results prove that the suggested method outperforms the existing approaches in terms of accuracy, convergence speed, noise robustness, and efficiency.

关键词： remora optimization algorithm Chimp optimization algorithm Multi-level thresholding image segmentation Cross-entropy

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An Efficient Approach Based on remora optimization algorithm and Levy Flight for Intrusion Detection

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Intelligent Automation & Soft Computing 2023年第7期37卷 235-254页

作者： Abdullah Mujawib Alashjaee Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Kingdom of Saudi Arabia

With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack ***,feature selection is a vital preprocessing stage in machine learning *** paper presents a novel feature selection-based approach,remora optimization algorithm-Levy Flight(ROA-LF),to improve intrusion detection by boosting the ROA performance with *** developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection:Knowledge discovery and data mining tools competition,network security laboratory knowledge discovery and data mining,intrusion detection evaluation dataset,block out traffic network,Canadian institute of cybersecu-rity and three engineering problems:Cantilever beam design,three-bar truss design,and pressure vessel design.A comparative analysis between developed ROA-LF,particle swarm optimization,salp swarm algorithm,snake opti-mizer,and the original ROA methods is also *** results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.

关键词： Feature selection metaheuristic algorithms intrusion detection remora optimization algorithm Levy flight

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Design of advanced intrusion detection in cybersecurity using ensemble of deep learning models with an improved beluga whale optimization algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2025年 121卷 90-102页

作者： Alhayan, Fatimah Alruwais, Nuha Alamgeer, Mohammad Alashjaee, Abdullah M. Abdullah, Monir Khadidos, Alaa O. Alallah, Fouad Shoie Alshareef, Abdulrhman Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn POB 22459 Riyadh 11495 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Informat Syst Abha Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar Saudi Arabia Univ Bisha Coll Comp & Informat Technol Dept Comp Sci & Artificial Intelligence Bisha 67714 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia

The rapid growth and evolution of the Internet over the last few decades caused more concern about frequently changing and increasing cyberattacks. The Intrusion Detection System (IDS) is a powerful tool applied in cybersecurity methods to identify and detect intrusion attacks. Also, the probability of several intrusion attacks rises with the enormous data generation. Feature selection (FS) is vital and essential for improving performance. The dataset structure can influence the efficacy of the machine learning (ML) method. Besides, data imbalance can pose a problem, but sampling techniques can assist in reducing it. As an outcome, an efficient IDS was needed to protect the data, and the innovation of artificial intelligence's (AI) sub-domains, ML, and deep learning (DL) was one of the most effective methods to deal with this issue. Therefore, this study develops an Enhanced Intrusion Detection in Cybersecurity Using Ensemble Learning with Improved Beluga Whale optimization (IDCSELIBWO) technique. The proposed IDCS-ELIBWO technique mainly addresses the detection of intrusions to achieve cybersecurity in a network. In the IDCS-ELIBWO approach, the main phase of data normalization employing min-max normalization is performed. The remora optimization algorithm (ROA) is utilized for FS and diminishes computation complexity. For cybersecurity detection, the IDCS-ELIBWO technique employs ensemble learning classifiers containing three methods such as deep belief network (DBN), gated recurrent unit (GRU), and long short-term memory (LSTM). At last, an improved beluga whale optimization (IBWO) method is used for the hyperparameter tuning process. An extensive experiment is conducted to examine the improved performance of the proposed IDCS-ELIBWO method. The performance validation of the IDCS-ELIBWO technique portrayed a superior accuracy value of 99.77% over recent methods.

关键词： Intrusion Detection Cybersecurity remora optimization algorithm Ensemble Learning Parameter Selection

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Hybridizing remora and aquila optimizer with dynamic oppositional learning for structural engineering design problems

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 462卷

作者： Varshney, Megha Kumar, Pravesh Abualigah, Laith Rajkiya Engn Coll Bijnor 246725 India Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

To solve global optimization problems, the Aquila Optimizer (AO) algorithm was created recently and is based on the hunting habits of Aquila birds. The remora optimization algorithm (ROA) is combined with a novel Aquila optimizer in this study to create a hybrid version that generates new local solutions based on the best available ones, thereby improving searchability. Additionally, the implementation of dynamic oppositional-based learning (DOL) techniques facilitates both the exploration and exploitation of a search field while preserving an appropriate balance between them. Designated RODAO, is the proposed algorithm. The fundamental characteristic of the proposed approach is the use of remora's ability to prevent premature convergence and local search problems, as well as the DOL strategy to preserve high-quality solutions and variety among the RODAO's solutions. In order to assess these competencies in RODAO, the IEEE CEC 2017 benchmark functions as well as a traditional set of well-known benchmark functions have been used. The robustness and efficiency of the method are guaranteed by a number of performance measurements used on RODAO, including statistical tests and convergence graphs. Three popular engineering optimization issues are also solved in the paper using the suggested RODAO technique. The analysis and numerical experiments show that real-world optimization issues can be successfully solved by the proposed algorithm or RODAO.

关键词： optimization Aquila optimizer remora optimization algorithm Dynamic oppositional learning Engineering optimization Problems

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