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In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon 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 in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html

License: MIT License

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slime-mould algorithm sma ali-asghar-heidari paper optimization metaheuristics evolutionary-algorithms swarm-intelligence slime

slime-mould-algorithm-a-new-method-for-stochastic-optimization-'s Introduction

Slime mould algorithm: A new method for stochastic optimization

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon 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 in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.aliasgharheidari.com//SMA.html

Slime mould algorithm: A new method for stochastic optimization Shimin Li, Huiling Chen, Mingjing Wang, Ali Asghar Heidari, Seyedali Mirjalili 2020/4/3 Future Generation Computer Systems DOI: https://doi.org/10.1016/j.future.2020.03.055 https://www.sciencedirect.com/science/article/pii/S0167739X19320941

Website of SMA: http://www.aliasgharheidari.com//SMA.html You can find and run the SMA code online at http://www.aliasgharheidari.com//SMA.html

You can find the SMA paper at https://doi.org/10.1016/j.future.2020.03.055 Please follow the paper for related updates in researchgate: https://www.researchgate.net/publication/340431861_Slime_mould_algorithm_A_new_method_for_stochastic_optimization

Main idea: Shimin Li Author and programmer: Shimin Li,Ali Asghar Heidari,Huiling Chen e-Mail: [email protected]

Co-author: Huiling Chen([email protected]) Mingjing Wang([email protected]) Ali Asghar Heidari([email protected], [email protected]) Seyedali Mirjalili

         Researchgate: Ali Asghar Heidari https://www.researchgate.net/profile/Ali_Asghar_Heidari
         Researchgate: Huiling Chen https://www.researchgate.net/profile/Huiling_Chen

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