Slime mould algorithm: A new method for stochastic optimization

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Li, Shimin
Chen, Huiling
Wang, Mingjing
Heidari, Ali Asghar
Mirjalili, Seyedali
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2020
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http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based on the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics using an extensive set of benchmarks to verify its efficiency. Moreover, four classical engineering problems are utilized to estimate the efficacy of the algorithm in optimizing constrained problems. The results demonstrate that the proposed SMA benefits from competitive, often outstanding performance on different search landscapes. The source codes of SMA are publicly available at http://www.alimirjalili.com/SMA.html and https://tinyurl.com/Slime-mould-algorithm.

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Future Generation Computer Systems
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111
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© 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
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Software engineering
Data management and data science
Distributed computing and systems software
Information systems
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Li, S; Chen, H; Wang, M; Heidari, AA; Mirjalili, S, Slime mould algorithm: A new method for stochastic optimization, Future Generation Computer Systems, 2020, 111, pp. 300-323
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