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An Enhanced Slime Mould Optimizer That Uses Chaotic Behavior and an Elitist Group for Solving Engineering Problems

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  • Shahenda Sarhan

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt)

  • Abdullah Mohamed Shaheen

    (Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Ragab A. El-Sehiemy

    (Department of Electrical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

  • Mona Gafar

    (Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam Bin Abdulaziz University, Kharj 16278, Saudi Arabia
    Machine Learning and Information Retrieval Department, Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

Abstract

This article suggests a novel enhanced slime mould optimizer (ESMO) that incorporates a chaotic strategy and an elitist group for handling various mathematical optimization benchmark functions and engineering problems. In the newly suggested solver, a chaotic strategy was integrated into the movement updating rule of the basic SMO, whereas the exploitation mechanism was enhanced via searching around an elitist group instead of only the global best dependence. To handle the mathematical optimization problems, 13 benchmark functions were utilized. To handle the engineering optimization problems, the optimal power flow (OPF) was handled first, where three studied cases were considered. The suggested scheme was scrutinized on a typical IEEE test grid, and the simulation results were compared with the results given in the former publications and found to be competitive in terms of the quality of the solution. The suggested ESMO outperformed the basic SMO in terms of the convergence rate, standard deviation, and solution merit. Furthermore, a test was executed to authenticate the statistical efficacy of the suggested ESMO-inspired scheme. The suggested ESMO provided a robust and straightforward solution for the OPF problem under diverse goal functions. Furthermore, the combined heat and electrical power dispatch problem was handled by considering a large-scale test case of 84 diverse units. Similar findings were drawn, where the suggested ESMO showed high superiority compared with the basic SMO and other recent techniques in minimizing the total production costs of heat and electrical energies.

Suggested Citation

  • Shahenda Sarhan & Abdullah Mohamed Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "An Enhanced Slime Mould Optimizer That Uses Chaotic Behavior and an Elitist Group for Solving Engineering Problems," Mathematics, MDPI, vol. 10(12), pages 1-30, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:1991-:d:834756
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    References listed on IDEAS

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    1. Kanhua Yu & Lili Liu & Zhe Chen, 2021. "An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources," Mathematics, MDPI, vol. 9(12), pages 1-26, June.
    2. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Elattar, Ehab & Ginidi, Ahmed R., 2022. "An Amalgamated Heap and Jellyfish Optimizer for economic dispatch in Combined heat and power systems including N-1 Unit outages," Energy, Elsevier, vol. 246(C).
    3. Zhang, Jingrui & Wang, Silu & Tang, Qinghui & Zhou, Yulu & Zeng, Tao, 2019. "An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems," Energy, Elsevier, vol. 172(C), pages 945-957.
    4. Mostafa Abdo & Salah Kamel & Mohamed Ebeed & Juan Yu & Francisco Jurado, 2018. "Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer," Energies, MDPI, vol. 11(7), pages 1-16, June.
    5. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    6. Abdullah Shaheen & Ahmed Ginidi & Ragab El-Sehiemy & Abdallah Elsayed & Ehab Elattar & Hassen T. Dorrah, 2022. "Developed Gorilla Troops Technique for Optimal Power Flow Problem in Electrical Power Systems," Mathematics, MDPI, vol. 10(10), pages 1-29, May.
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    Citations

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    Cited by:

    1. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2022. "A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies," Mathematics, MDPI, vol. 10(13), pages 1-26, June.
    2. Ghareeb Moustafa & Ali M. El-Rifaie & Idris H. Smaili & Ahmed Ginidi & Abdullah M. Shaheen & Ahmed F. Youssef & Mohamed A. Tolba, 2023. "An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(15), pages 1-26, July.
    3. Slim Abid & Ali M. El-Rifaie & Mostafa Elshahed & Ahmed R. Ginidi & Abdullah M. Shaheen & Ghareeb Moustafa & Mohamed A. Tolba, 2023. "Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems," Mathematics, MDPI, vol. 11(8), pages 1-32, April.
    4. Mohamed S. Hashish & Hany M. Hasanien & Zia Ullah & Abdulaziz Alkuhayli & Ahmed O. Badr, 2023. "Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty," Sustainability, MDPI, vol. 15(18), pages 1-27, September.
    5. Shahenda Sarhan & Abdullah M. Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems," Mathematics, MDPI, vol. 10(13), pages 1-22, July.

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