Multi-strategy Slime Mould Algorithm for hydropower multi-reservoir systems optimization

Ahmadianfar, Iman and Noori, Ramzia Majeed and Togun, Hussein and Falah, Mayadah W. and Homod, Raad Z. and Fu, Minglei and Halder, Bijay and Deo, Ravinesh ORCID: https://orcid.org/0000-0002-2290-6749 and Yaseen, Zaher Mundher (2022) Multi-strategy Slime Mould Algorithm for hydropower multi-reservoir systems optimization. Knowledge-based Systems, 250:109048. pp. 1-18. ISSN 0950-7051


Abstract

The challenge to determine the best policies for hydropower multiple reservoir systems is a high-dimensional and nonlinear problem, making it challenging to attain a global solution. To efficiently optimize such a complicated solution, the creation of a high-precision optimization algorithm is critical. Hence, this research proposes a Multi-strategy Slime Mould Algorithm (MSMA) to determine the optimal operating rules for a complicated hydropower multiple reservoir prediction problem. The MSMA system proposed employs an effective wrap food mechanism to strengthen local and global capability; an enhanced solution quality (ESQ) to promote solution quality; and the interior-point method to implement an influential exploitation mechanism. The numerical testing of 23 test functions demonstrates the efficiency of the MSMA algorithm in solving global optimization issues. The newly developed method is then used to optimize the operation of a complex eight-reservoir hydropower system, with the proposed MSMA approach resulting in 0.999% of an ideal global solution, according to the optimal findings. The results of the multi-reservoir system show that proposed MSMA method was able to generate about 16.6% more power than the SMA. Consequently, the recommended method outperforms the other well-known optimization methods for maximizing power in the multi-reservoir system. Finally, this study also provides a useful tool for optimizing the complicated hydropower multiple reservoir problems.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 07 Jun 2022 01:17
Last Modified: 07 Jun 2022 01:17
Uncontrolled Keywords: Optimization; Hydropower; Multi-reservoir; Operation; Slime Mould Algorithm; Multi-strategy
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
40 ENGINEERING > 4008 Electrical engineering > 400803 Electrical energy generation (incl. renewables, excl. photovoltaics)
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
Identification Number or DOI: https://doi.org/10.1016/j.knosys.2022.109048
URI: http://eprints.usq.edu.au/id/eprint/48725

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