Success Fail History Based Hybrid RUNgeKutta Optimizer and Slime Mould Algorithm

doi:10.3778/j.issn.1002-8331.2111-0546

Abstract

Abstract: A success fail history based hybrid RUNgeKutta optimizer and slime mould algorithm is proposed to solve the shortcoming of low convergence speed and low precision of the basic slime mould algorithm. Firstly, an improved success fail history archive mechanism for storing position information is proposed, and the change in individual fitness value is used as the probability of each archive record participating in calculations, and this mechanism is added to the original slime mould algorithm. Secondly, the RUNgeKutta optimizer and the improved slime mould algorithm are merged through parallel computing and information exchange to guide the slime mould algorithm to jump out of the local optimum, and to improve the slime mould algorithm in iterative when the accuracy of the solution in the narrow space. Thirdly, a communication strategy combining long and short time intervals is proposed to determine the timing of the communication between the two populations. Finally, a series of population information communication mechanisms based on space movement are proposed, without affecting two algorithms’ characteristics and advantages, the limitations of the two algorithms are overcome at the same time. For the experiment part, it uses the CEC2017 benchmark test functions, with traditional statistical indicators, MAE ranking, and Wilcoxon rank-sum test to verify the effectiveness of the new algorithm. At the same time, it explores high-dimensional functions and compares with the state-of-the-art swarm intelligence algorithms and improved algorithms in recent years. Also, the incomplete algorithms of this algorithm are compared and tested. The experimental results show that the improved strategy in this paper is effective and has portability, this algorithm’s accuracy and robustness are more competitive.

Key words: slime mould algorithm, success fail history, RUNgeKutta optimizer, CEC2017

摘要： 针对黏菌算法收敛速度慢和求解精度低等问题，提出了基于成败历史存档的融合龙格库塔-黏菌算法。提出了一种存储位置信息的改进成败历史存档机制，并使用个体适应度值的变化量作为每个存档记录参与后续计算的概率，将该机制加入原始黏菌算法；将龙格库塔算法与改进的黏菌算法通过并行计算-信息交流的方式进行融合，引导黏菌算法跳出局部最优，提升黏菌算法在狭小空间中的求解精度；提出了长短时间间隔结合的交流策略，用以确定两种群交流的时机；提出了一系列基于空间移动的种群信息交流机制，在保留两算法各自特性和优势的情况下，同时克服两算法的局限性。实验部分使用了CEC2017基准测试函数，使用了传统统计特征和MAE排名、Wilcoxon秩和检验验证算法有效性，同时对高维度函数进行探索，并与近年来新颖的高水平群智能算法、改进算法进行对比测试，实验结果表明该改进策略有效且具有一定可迁移性，改进后算法的求解精度和鲁棒性更具竞争力。

关键词: 黏菌算法, 成败历史存档, 龙格库塔算法, CEC2017

LIU Yusong, LIU Sheng. Success Fail History Based Hybrid RUNgeKutta Optimizer and Slime Mould Algorithm[J]. Computer Engineering and Applications, 2022, 58(17): 61-71.

刘宇凇, 刘升. 成败历史存档的融合龙格库塔-黏菌算法[J]. 计算机工程与应用, 2022, 58(17): 61-71.

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