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A multi-strategy enhanced reptile search algorithm for global optimization and engineering optimization design problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-41页

作者： Zhou, Liping Liu, Xu Tian, Ruiqing Wang, Wuqi Jin, Guowei Zhejiang Coll Secur Technol Coll Emergency Technol 2555 Ouhai Ave Wenzhou 325016 Zhejiang Peoples R China Shanghai Univ Finance & Econ 777 Guoding Rd Shanghai 200433 Peoples R China Zhejiang Coll Secur Technol Coll New Energy Equipment 2555 Ouhai Ave Wenzhou 325016 Zhejiang Peoples R China

The reptile search algorithm (RSA) is a well-known swarm-based metaheuristic algorithm inspired by the hunting behaviors of crocodiles. To overcome the problems of falling into local optima and premature convergence, this paper proposes a multi-strategy enhanced reptile search algorithm (MRSA), which integrates a novel dynamic evolutionary sense, prey approaching strategy and Cauchy mutation strategy. The prey approaching strategy comes from the secretary bird optimization algorithm and is applied to strengthen the exploration capability of RSA. A comparative performance analysis is conducted using the CEC2005, CEC2017 and CEC2022 benchmark functions. And fifteen algorithms are employed for the performance comparison. The results of numerical, convergence curves, boxplots, Wilcoxon rank-sum test and Friedman ranking confirm the efficacy and stability of proposed MRSA, indicating its superior performance compared to other algorithms. Moreover, seven practical engineering design tasks are used to test the performance of MRSA in real-world optimization problems. The results also show that MRSA can efficiently obtain better optimal solution compared to existing methods.

关键词： reptile search algorithm Global optimization Secretary bird optimization algorithm Cauchy mutation Engineering design problems

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Integrated Local search Technique With reptile search algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

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OPTIMAL CONTROL APPLICATIONS & METHODS 2025年第2期46卷 775-788页

作者： Abu-Hashem, Muhannad A. Shehab, Mohammad Shambour, Mohd Khaled Abualigah, Laith King Abdulaziz Univ Architecture & Planning Fac Dept Geomat Jeddah Saudi Arabia Amman Arab Univ Coll Comp Sci & Informat Amman Jordan Middle East Univ Fac Engn & Design Dept Intelligent Syst Engn Amman Jordan Al Al Bayt Univ Comp Sci Dept Mafraq Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura Punjab India Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk Saudi Arabia Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya Malaysia

The reptile search algorithm (RSA) is a powerful modern optimization technique that effectively solves intricate problems across various fields. Despite its notable success, the local search aspect of RSA requires enhancement to overcome issues such as limited solution variety, a pattern of falling into local optimal traps, and the possibility of early convergence. In response to these challenges, this research introduces an innovative paradigm that melds the robust and time-honoured local search technique, Simulated Annealing (SA), with RSA, christened henceforth as SARSA. This amalgamation aims to tackle the qualities of both strategies, synergistically improving their optimization capabilities. We utilize a broad and thorough assessment system to survey the viability and strength of SARSA. A comprehensive cluster of benchmark issues sourced from the CEC 2019 benchmark suite and an assorted set of real-world challenges drawn from the CEC 2011 store is utilized as the test bed. This fastidiously curated testbed guarantees an intensive examination of SARSA's execution over a wide range of issues and complexities. Our observational discoveries substantiate that SARSA beats the foundational RSA and a few related calculations reported within the existing body of writing, in this manner setting up SARSA as a critical progression in optimization calculations. The prevalent execution illustrated by SARSA highlights its potential for broad application and underscores its utility in handling complex optimization issues viably.

关键词： local search techniques optimization algorithms real-world problems reptile search algorithm simulated annealing

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Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding

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SCIENTIFIC REPORTS 2025年第1期15卷 1-45页

作者： Abualigah, Laith Al-Okbi, Nada Khalil Alomari, Saleh Ali Almomani, Mohammad H. Moneam, Sahar Yousif, Maryam A. Snasel, Vaclav Saleem, Kashif Smerat, Aseel Ezugwu, Absalom E. Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Univ Baghdad Coll Sci Women Dept Comp Sci Baghdad Iraq Jadara Univ Fac Informat Technol Irbid 21110 Jordan VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Poruba Ostrava 70800 Czech Republic King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh 11362 Saudi Arabia Al Ahliyya Amman Univ Fac Educ Sci Amman 19328 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India North West Univ Unit Data Sci & Comp 11 Hofman St ZA-2520 Potchefstroom South Africa Hashemite Univ Dept Math Fac Sci POB 330127 Zarqa 13133 Jordan Mazaya Univ Coll Comp Technol Engn Nasiriyah Iraq

Image segmentation using bi-level thresholds works well for straightforward scenarios;however, dealing with complex images that contain multiple objects or colors presents considerable computational difficulties. Multi-level thresholding is crucial for these situations, but it also introduces a challenging optimization problem. This paper presents an improved reptile search algorithm (RSA) that includes a Gbest operator to enhance its performance. The proposed method determines optimal threshold values for both grayscale and color images, utilizing entropy-based objective functions derived from the Otsu and Kapur techniques. Experiments were carried out on 16 benchmark images, which included COVID-19 scans along with standard color and grayscale images. A thorough evaluation was conducted using metrics such as the fitness function, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the Friedman ranking test. The results indicate that the proposed algorithm seems to surpass existing state-of-the-art methods, demonstrating its effectiveness and robustness in multi-level thresholding tasks.

关键词： Medical images Image segmentation Multi-level threshold reptile search algorithm Otsu method, Kapur method

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Enhancing Damage Detection Using reptile search algorithm-Optimized Neural Network and Frequency Response Function

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第1期13卷 1-19页

作者： Khatir, A. Capozucca, R. Khatir, S. Magagnini, E. Cuong-Le, Thanh Polytech Univ Marche Struct Sect DICEA Via Brecce Bianche 12 I-60131 Ancona Italy Univ Sci & Technol Mohamed Boudiaf Oran USTO MB Civil Engn Dept LM2SC Lab 1505EL MNAOUER Oran 31000 Algeria Ho Chi Minh City Open Univ Ctr Engn Applicat & Technol Solut Ho Chi Minh Vietnam

PurposeThis study aims to enhance structural damage detection in steel beams by optimizing Artificial Neural Network (ANN) training using the reptile search algorithm (RSA). The hybrid ANN-RSA model is proposed to address limitations such as local optima entrapment and constrained convergence accuracy. Frequency Response Function (FRF) data is utilized as input to improve the precision of damage location *** data was obtained from experimental vibration analysis and validated through Finite Element (FE) modeling. Damage scenarios were simulated by introducing notches of identical dimensions at varying positions on steel beams. The RSA-optimized ANN model's performance was compared against alternative models, including Particle Swarm Optimization (ANN-PSO), Artificial Bee Colony (ANN-ABC), and Genetic algorithm (ANN-GA). Key metrics like prediction accuracy, regression values, and computational efficiency were *** hybrid ANN-RSA model demonstrated superior performance, achieving a regression value nearing 1 and reducing prediction errors by approximately 15% compared to ANN-PSO, ANN-ABC, and ANN-GA models. For all damage scenarios, the model exhibited consistent accuracy with error margins below 1%, outperforming competitors in both computational speed and precision. The ANN-RSA model required fewer iterations for convergence, highlighting its computational efficiency. Comparative analysis showed the RSA method effectively optimized ANN parameters, improving damage localization reliability and *** integration of RSA into ANN training significantly improves the accuracy and computational efficiency of damage prediction in structural health monitoring. The use of FRF data as input ensures minimal information leakage and enhances model precision.

关键词： Damage detection Vibration analysis ANN FRF reptile search algorithm Finite element method

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Enhancing reptile search algorithm Performance for the Knapsack Problem with Integration of Chaotic Map 23rd

Enhancing Reptile Search Algorithm Performance for the Knaps...

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23rd Mexican International Conference on Artificial Intelligence

作者： Barrera-Garcia, Jose Cisternas-Caneo, Felipe Crawford, Broderick Soto, Ricardo Becerra-Rozas, Marcelo Giachetti, Giovanni Monfroy, Eric Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Andres Bello Santiago Chile Univ Angers LERIA Angers France

ISBN: (纸本)9783031755422;9783031755439

This study investigates the binarization process of the reptile search algorithm (RSA) using chaotic maps to solve the Knapsack Problem. We evaluate RSA, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) using the S4 transfer function with four binarization strategies: standard, standard with chaotic maps, elitist, and elitist with chaotic maps. Experimental results show that standard binarization strategies, particularly RSA with standard binarization rule (STD) and RSA with standard binarization rule with a chaotic map (STD SINE), consistently outperform elitist strategies across various Knapsack problem instances. Including chaotic maps, especially the sine chaotic map, slightly improves performance. Convergence analysis reveals that standard binarization ensures steady and strong convergence, while elitist binarization accelerates convergence but may risk settling on local optima early. This research highlights the importance of selecting appropriate binarization strategies and suggests further exploration of chaotic maps to enhance the performance of metaheuristic algorithms in solving binary combinatorial optimization problems.

关键词： reptile search algorithm Combinatorial Problems Binarization Schemes Chaotic Maps Metaheuristics

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reptile search algorithm and kriging surrogate model for structural design optimization with natural frequency constraints

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MATERIALS TESTING 2022年第10期64卷 1504-1511页

作者： Yildiz, Betul Sultan Bureerat, Sujin Panagant, Natee Mehta, Pranav Yildiz, Ali Riza Bursa Uludag Univ Dept Mech Engn TR-16059 Bursa Turkey Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 Gujarat India

This study explores the use of a recent metaheuristic algorithm called a reptile search algorithm (RSA) to handle engineering design optimization problems. It is the first application of the RSA to engineering design problems in literature. The RSA optimizer is first applied to the design of a bolted rim, which is constrained optimization. The developed algorithm is then used to solve the optimization problem of a vehicle suspension arm, which aims to solve the weight reduction under natural frequency constraints. As function evaluations are achieved by finite element analysis, the Kriging surrogate model is integrated into the RSA algorithm. It is revealed that the optimum result gives a 13% weight reduction compared to the original structure. This study shows that RSA is an efficient metaheuristic as other metaheuristics such as the mayfly optimization algorithm, battle royale optimization algorithm, multi-level cross-entropy optimizer, and red fox optimization algorithm.

关键词： battle royale optimization algorithm brake pedal engineering structures mayfly optimization algorithm multi-level cross-entropy optimizer optimization red fox optimization algorithm reptile search algorithm

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Improved reptile search algorithm by Salp Swarm algorithm for Medical Image Segmentation

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Journal of Bionic Engineering 2023年第4期20卷 1766-1790页

作者： Laith Abualigah Mahmoud Habash Essam Said Hanandeh Ahmad MohdAziz Hussein Mohammad Al Shinwan Raed Abu Zitar Heming Jia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Amman Arab University AmmanJordan Department of Computer Information System Zarqa UniversityP.O.Box 13132ZarqaJordan Deanship of E-Learning and Distance Education Umm Al-Qura UniversityMakkah21955Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates School of Information Engineering Sanming UniversitySanming365004China

This study proposes a novel nature-inspired meta-heuristic optimizer based on the reptile search algorithm combed with Salp Swarm algorithm for image segmentation using gray-scale multi-level thresholding,called *** proposed method introduces a better search space to find the optimal solution at each ***,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level *** obtained solutions by the proposed method are represented using the image *** proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each *** performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking *** benchmark images of COVID-19 validate the performance of the proposed *** results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

关键词： Bioinspired reptile search algorithm Salp Swarm algorithm Multi-level thresholding Image segmentation Meta-heuristic algorithm

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Feedback Mechanism-driven Mutation reptile search algorithm for Optimizing Interpolation Developable Surfaces

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Journal of Bionic Engineering 2024年第1期21卷 527-571页

作者： Gang Hu Jiao Wang Xiaoni Zhu Muhammad Abbas Department of Applied Mathematics Xi'an University of TechnologyXi'an 710054China School of Mechanical and Precision Instrument Engineering Xi'an University of TechnologyXi'an 710048China Department of Mathematics University of SargodhaSargodha 40100Pakistan

Curvature lines are special and important curves on *** is of great significance to construct developable surface interpolated on curvature lines in engineering *** this paper,the shape optimization of generalized cubic ball developable surface interpolated on the curvature line is studied by using the improved reptile search ***,based on the curvature line of generalized cubic ball curve with shape adjustable,this paper gives the construction method of SGC-Ball developable surface interpolated on the ***,the feedback mechanism,adaptive parameters and mutation strategy are introduced into the reptile search algorithm,and the Feedback mechanism-driven improved reptile search algorithm effectively improves the solving *** IEEE congress on evolutionary computation 2014,2017,2019 and four engineering design problems,the feedback mechanism-driven improved reptile search algorithm is compared with other representative methods,and the result indicates that the solution performance of the feedback mechanism-driven improved reptile search algorithm is *** last,taking the minimum energy as the evaluation index,the shape optimization model of SGC-Ball interpolation developable surface is *** developable surface with the minimum energy is achieved with the help of the feedback mechanism-driven improved reptile search algorithm,and the comparison experiment verifies the superiority of the feedback mechanism-driven improved reptile search algorithm for the shape optimization problem.

关键词： reptile search algorithm Feedback mechanism Adaptive parameter Mutation strategy SGC-Ball interpolation developable surface Shape optimization

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Development of Levy flight-based reptile search algorithm with local search ability for power systems engineering design problems

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NEURAL COMPUTING & APPLICATIONS 2022年第22期34卷 20263-20283页

作者： Ekinci, Serdar Izci, Davut Abu Zitar, Raed Alsoud, Anas Ratib Abualigah, Laith Batman Univ Dept Comp Engn TR-72100 Batman Turkey Batman Univ Dept Elect & Automat TR-72060 Batman Turkey Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi 38044 U Arab Emirates Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Middle East Univ Fac Informat Technol Amman 11831 Jordan

The need for better-performing algorithms to solve real-world power systems engineering problems has always been a challenging topic. Due to their stochastic nature, metaheuristic algorithms can provide better results. Thus, they have a rising trend in terms of investigation. This paper is a further attempt to offer a better optimizing structure, therefore, aims to provide a better-performing algorithm both for designing an appropriate proportional-integral-derivative (PID) controller to effectively operate an automatic voltage regulator (AVR) system and extracting the optimum parameters of a power system stabilizer (PSS) employed in a single-machine infinite-bus (SMIB) power system. Therefore, the paper discusses the development of the Levy flight-based reptile search algorithm with local search capability and evaluates its potential against challenging power systems engineering optimization problems. The Levy flight concept is used for better exploration capability in the proposed algorithm, whereas the Nelder-Mead simplex search algorithm is integrated for further exploitation. The latter case is confirmed through 23 benchmark functions with different features using statistical and nonparametric tests. The superiority of the proposed Levy flight-based reptile search and Nelder-Mead (L-RSANM) algorithm-based PID controller for the AVR system is demonstrated comparatively using convergence, statistical and nonparametric tests along with transient and frequency responses. Besides, it is also assessed against previously reported and different methods, showing further superiority for AVR system control. Furthermore, the extraordinary ability of the L-RSANM algorithm to design an efficient PSS employed in the SMIB power system is demonstrated, as well. In conclusion, the proposed L-RSANM algorithm is shown to be more capable to solve the challenging power systems engineering design problems.

关键词： reptile search algorithm Levy flight Nelder-Mead method Electric power systems

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An improved reptile search algorithm based on mathematical optimization accelerator and elementary functions

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期45卷 4179-4208页

作者： Zheng, Yue Xing, Cheng Wang, Jie-Sheng Song, Hao-Ming Bao, Yin-Yin Zhang, Xing-Yue Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Peoples R China

The reptile search algorithm (RSA) is a dynamic and effective meta-heuristic algorithm inspired by the behavior of crocodiles in nature and the way of hunting prey. Unlike other crawler search algorithms, it uses four novel mechanisms to update the location of the solutions, such as walking at high or on the belly, and hunting in a coordinated or cooperative manner. In this algorithm, the total number of iterations is divided into four intervals, and different position-updating strategies are used to make the algorithm easily fall into the local optimum. Therefore, an improved reptile search algorithm based on a mathematical optimization accelerator (MOA) and elementary functions is proposed to improve its search efficiency and make it not easily fall into local optimum. MOA was used to realize the switching of RSA's four searching modes by introducing random perturbations of six elementary functions (sine function, cosine function, tangent function, arccosine function, hyperbolic secant function and hyperbolic cosecant function), four mechanisms are distinguished by random number instead of the original RSA algorithm's inherent four mechanisms by iteration number, which increases the randomness of the algorithm and avoids falling into local optimum. The random perturbations generated by elementary functions are added to the variation trend of parameter MOA to improve the optimization accuracy of the algorithm. To verify the effectiveness of the proposed algorithm, 30 benchmark functions in CEC2017 were used for carrying out simulation experiments, and the optimization performance was compared with BAT, PSO, ChOA, MRA and SSA. Finally, two practical engineering design problems are optimized. Simulation results show that the proposed sechRSA has strong global optimization ability.

关键词： reptile search algorithm mathematically optimized accelerator elementary function function optimization engineering optimization

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