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Relevant bibliographies by topics / Stochastic fractal search

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Academic literature on the topic 'Stochastic fractal search'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Stochastic fractal search"

1

Lagunes, Marylu L., Oscar Castillo, Fevrier Valdez, Jose Soria, and Patricia Melin. "A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation." Fractal and Fractional 5, no. 2 (2021): 33. http://dx.doi.org/10.3390/fractalfract5020033.

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Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

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Juybari, Mohammad N., Mostafa Abouei Ardakan, and Hamed Davari-Ardakani. "A penalty-guided fractal search algorithm for reliability–redundancy allocation problems with cold-standby strategy." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 5 (2019): 775–90. http://dx.doi.org/10.1177/1748006x19825707.

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This article addresses the system reliability optimization problem as reliability–redundancy allocation problem, aiming to maximize the system reliability through a trade-off between redundancy levels and the reliability of the components. In this study, cold-standby strategy has been considered for the redundant components, and a population-based meta-heuristic algorithm, called stochastic fractal search, is applied to solve different benchmark problems. Using the proposed stochastic fractal search algorithm, all the benchmark problems are improved and new structures with higher reliability values have been found. The experimental results reveal the superiority of the proposed stochastic fractal search algorithm in terms of quality and robustness of the solutions in cold-standby redundancy case compared to all previous studies.

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3

Salimi, Hamid. "Stochastic Fractal Search: A powerful metaheuristic algorithm." Knowledge-Based Systems 75 (February 2015): 1–18. http://dx.doi.org/10.1016/j.knosys.2014.07.025.

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Luo, Qifang, Sen Zhang, and Yongquan Zhou. "Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition." Scientific Programming 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1803934.

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Template matching is a basic and crucial process for image processing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-SFS. SFS is a new metaheuristic algorithm inspired by random fractals. Furthermore, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. In this work, lateral inhibition is employed for image preprocessing. LI-SFS takes both the advantages of SFS and lateral inhibition which leads to better performance. Our simulation results show that LI-SFS is more effective and robust for this template matching mission than other algorithms based on LI.

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Alomoush, Muwaffaq I., and Zaid B. Oweis. "Environmental-economic dispatch using stochastic fractal search algorithm." International Transactions on Electrical Energy Systems 28, no. 5 (2018): e2530. http://dx.doi.org/10.1002/etep.2530.

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Pham, Ly Huu, Thang Trung Nguyen, Lam Duc Pham, and Nam Hoang Nguyen. "Stochastic fractal search based method for economic load dispatch." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 5 (2019): 2535. http://dx.doi.org/10.12928/telkomnika.v17i5.12539.

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Chen, Xu, Hong Yue, and Kunjie Yu. "Perturbed stochastic fractal search for solar PV parameter estimation." Energy 189 (December 2019): 116247. http://dx.doi.org/10.1016/j.energy.2019.116247.

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Ly, Huu Pham, Trung Nguyen Thang, Duc Pham Lam, and Hoang Nguyen Nam. "Stochastic fractal search based method for economic load dispatch." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 5 (2019): 2535–46. https://doi.org/10.12928/TELKOMNIKA.v17i5.12539.

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This paper presents a nature-inspired meta-heuristic, called a stochastic fractal search based method (SFS) for coping with complex economic load dispatch (ELD) problem. Two SFS methods are introduced in the paper by employing two different random walk generators for diffusion process in which SFS with Gaussian random walk is called SFS-Gauss and SFS with Levy Flight random walk is called SFS-Levy. The performance of the two applied methods is investigated comparing results obtained from three test system. These systems with 6, 10, and 20 units with different objective function forms and different constraints are inspected. Numerical result comparison can confirm that the applied approach has better solution quality and fast convergence time when compared with some recently published standard, modified, and hybrid methods. This elucidates that the two SFS methods are very favorable for solving the ELD problem.

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Sun, Xiaohan, Anlin Li, Shaoxiang Zhu, and Feng Zhu. "Random Walk on T-Fractal with Stochastic Resetting." Entropy 26, no. 12 (2024): 1034. https://doi.org/10.3390/e26121034.

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In this study, we explore the impact of stochastic resetting on the dynamics of random walks on a T-fractal network. By employing the generating function technique, we establish a recursive relation between the generating function of the first passage time (FPT) and derive the relationship between the mean first passage time (MFPT) with resetting and the generating function of the FPT without resetting. Our analysis covers various scenarios for a random walker reaching a target site from the starting position; for each case, we determine the optimal resetting probability γ* that minimizes the MFPT. We compare the results with the MFPT without resetting and find that the inclusion of resetting significantly enhances the search efficiency, particularly as the size of the network increases. Our findings highlight the potential of stochastic resetting as an effective strategy for the optimization of search processes in complex networks, offering valuable insights for applications in various fields in which efficient search strategies are crucial.

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M. Eid, Marwa, Fawaz Alassery, Abdelhameed Ibrahim, Bandar Abdullah Aloyaydi, Hesham Arafat Ali, and Shady Y. El-Mashad. "Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation." Computers, Materials & Continua 72, no. 2 (2022): 2467–82. http://dx.doi.org/10.32604/cmc.2022.025220.

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Book chapters on the topic "Stochastic fractal search"

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Bingöl, Okan, Serdar Paçacı, and Uğur Güvenç. "Entropy-Based Skin Lesion Segmentation Using Stochastic Fractal Search Algorithm." In Artificial Intelligence and Applied Mathematics in Engineering Problems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36178-5_69.

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Dubey, Hari Mohan, Manjaree Pandit, B. K. Panigrahi, and Tushar Tyagi. "Multi-objective Power Dispatch Using Stochastic Fractal Search Algorithm and TOPSIS." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48959-9_14.

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Shahid, Mohammad, Mohd Shamim Ansari, Mohd Shamim, and Zubair Ashraf. "A Stochastic Fractal Search Based Approach to Solve Portfolio Selection Problem." In Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6407-6_41.

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Lagunes, Marylu L., Oscar Castillo, Fevrier Valdez, and Jose Soria. "Stochastic Fractal Dynamic Search for the Optimization of CEC’2017 Benchmark Functions." In Hybrid Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_35.

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Xu, Zhanxing, Jianzhong Zhou, Yuqi Yang, and Zhou Qin. "Improved Stochastic Fractal Search Algorithm for Joint Optimal Operation of Cascade Hydropower Stations." In Sustainable Development of Water and Environment. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07500-1_2.

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Lagunes, Marylu L., Oscar Castillo, Fevrier Valdez, Jose Soria, and Patricia Melin. "Optimization of Fuzzy Controllers for Autonomous Mobile Robots Using the Stochastic Fractal Search Method." In Recent Advances of Hybrid Intelligent Systems Based on Soft Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58728-4_10.

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Saini, Rita, Girish Parmar, Rajeev Gupta, and Afzal Sikander. "An Enhanced Tuning of PID Controller via Hybrid Stochastic Fractal Search Algorithm for Control of DC Motor." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7274-3_16.

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El Moutaouakil, Karim, Chellak Saliha, Baïzri Hicham, and Cheggour Mouna. "Intelligent Local Search Optimization Methods to Optimal Morocco Regime." In Swarm Intelligence - Recent Advances and Current Applications. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.105600.

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In this paper, we compare three well-known swarm algorithms on optimal regime based on our mathematical optimization model introduced recently. Different parameters of this latter are estimated based on 176 foods and on who’s the nutrients values are calculated for 100 g. The daily nutrients needs are estimated based on the expert’s knowledge. Different experimentations are realized for different configurations of the considered swarm algorithms. Compared to Stochastic Fractal Search (SFS) and Particle Swarm Optimization Algorithm (PSO), the Firefly Algorithm (FA) produces the main suitable regimes.

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Saini, Rita, Girish Parmar, and Rajeev Gupta. "An enhanced hybrid stochastic fractal search FOPID for speed control of DC motor." In Fractional Order Systems and Applications in Engineering. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-32-390953-2.00011-6.

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Ashraf, Zubair, Mohd Sarim Shamim, Raghib Noman, Mohammad Shahid, Faisal Ahmad, and Lamaan Sami. "Constrained Portfolio Optimization Using Hybrid Nature-Inspired Metaheuristic Algorithm in Stock Market." In Advances in Computer and Electrical Engineering. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6834-3.ch003.

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The optimal financial allocation has long been considered one of the essential aspects of risk-budgeted financial sector investment, maximizing returns and minimizing risks. Therefore, optimization techniques have been proposed to maximize the Sharpe ratio, to solve risk-budgeted-constrained portfolio selection problem. This paper presents a hybrid optimization method by embedding stochastic fractal search (SFS) in the artificial bee colony (ABC) to solve risk budgeted portfolio selection model. The diffusion and updating phases of SFS in collaboration with the employed bees, onlooker bees, and scout bee phases of ABC have significantly improved the ability to exploit the search in the proposed hybrid algorithm. The efficacy of the proposed hybrid algorithm is evaluated by using the NSE 50 of the Indian Stock Exchange market. The optimal Sharpe ratio of the assets has been obtained for several levels of risk budgeting, and the results are compared with the well-known metaheuristic optimization algorithms.

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Conference papers on the topic "Stochastic fractal search"

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Bingol, Okan, Ugur Guvenc, Serhat Duman, and Serdar Pacaci. "Stochastic fractal search with chaos." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090231.

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Rahman, Tuan A. Z., and M. Osman Tokhi. "Enhanced stochastic fractal search algorithm with chaos." In 2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2016. http://dx.doi.org/10.1109/icsgrc.2016.7813295.

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Van Hong, Thang Phan, Dieu Vo Ngoc, and Khanh Dang Tuan. "Environmental Economic Dispatch Using Stochastic Fractal Search Algorithm." In 2021 International Symposium on Electrical and Electronics Engineering (ISEE). IEEE, 2021. http://dx.doi.org/10.1109/isee51682.2021.9418796.

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Y. Nazaruddin, Yul, Franky Franky, and I. G. N. A. Indra Mandala. "OPTIMISASI PENGONTROL LQR MENGGUNAKAN ALGORITMA STOCHASTIC FRACTAL SEARCH." In Seminar Nasional Instrumentasi, Kontrol dan Otomasi 2018. Pusat Teknologi Instrumentasi dan Otomasi ITB, 2019. http://dx.doi.org/10.5614/sniko.2018.27.

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Van Hong, Thang Phan, Dieu Vo Ngoc, and Khanh Dang Tuan. "Optimization Microgrid System with Stochastic Fractal Search Algorithm." In 2022 IEEE Industrial Electronics and Applications Conference (IEACon). IEEE, 2022. http://dx.doi.org/10.1109/ieacon55029.2022.9951722.

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Yang, Jingbei, Jingming Sun, and Yuhao Yang. "Radar Task Scheduling Based on Stochastic Fractal Search." In 2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2023. http://dx.doi.org/10.1109/icemce60359.2023.10490993.

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Sasmito, Ayomi, and Asri Bekti Pratiwi. "Stochastic fractal search algorithm in permutation flowshop scheduling problem." In INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042196.

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Li, Wenguang, Shiyu Sun, Jianzeng Li, and Yongjiang Hu. "Stochastic Fractal Search Algorithm and its Application in Path Planning." In 2018 IEEE CSAA Guidance, Navigation and Control Conference (GNCC). IEEE, 2018. http://dx.doi.org/10.1109/gncc42960.2018.9018694.

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Mosbah, Hossam, and Mo El-Hawary. "Power System Static State Estimation Using Modified Stochastic Fractal Search Technique." In 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE). IEEE, 2018. http://dx.doi.org/10.1109/ccece.2018.8447826.

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Elrachid, Bendaoud, Radjeai Hammoud, Boutalbi Oussama, and Harbadji Meriem. "Parameter Tuning of Power Systems Stabilizer Using Stochastic Fractal Search Optimisation." In 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2022. http://dx.doi.org/10.1109/ssd54932.2022.9955763.

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