多策略融合的改进黏菌算法

Improved slime mould algorithm with multi-strategy fusion

针对标准黏菌算法(SMA)存在的容易陷入局部最优解、收敛速度慢以及求解精度低等问题,提出一种多策略融合的改进黏菌算法(MSISMA)。首先,引入布朗运动和莱维飞行机制以增强算法的搜索能力;其次,根据算法进行的不同阶段分别改进黏菌的位置更新公式,以提高算法的收敛速度和收敛精度;然后,应用区间自适应的反向学习(IAOBL)策略生成反向种群,以提升种群的多样性和质量,从而提高算法的收敛速度;最后,引入收敛停滞监测策略,当算法陷入局部最优时,通过对部分黏菌个体的位置重新初始化使算法跳出局部最优。选取23个测试函数,将MSISMA与平衡黏菌算法(ESMA)、黏菌-自适应引导差分进化混合算法(SMA-AGDE)、SMA、海洋捕食者算法(MPA)和平衡优化器(EO)进行测试和比较,并对算法运行结果进行Wilcoxon秩和检验。相较于对比算法,MSISMA在19个测试函数上获得最佳平均值,在12个测试函数上获得最佳标准差,优化精度平均提升23.39%~55.97%。实验结果表明,MSISMA的收敛速度、求解精度和鲁棒性明显较优。

Aiming at the problems of easily falling into local optimum,slow convergence and low solution accuracy of standard Slime Mould Algorithm(SMA),an Improved Slime Mould Algorithm with Multi-Strategy fusion(MSISMA)was proposed.Firstly,Brownian motion and Levy flight were introduced to enhance the search ability of the algorithm.Secondly,according to different stages of the algorithm,the location update formula of the slime mould was improved to increase the convergence speed and accuracy of the algorithm.Thirdly,the Interval Adaptative Opposition-Based Learning(IAOBL)strategy was adopted to generate the reverse population,with which the diversity and quality of the population were improved,as a result,the convergence speed of the algorithm was improved.Finally,a convergence stagnation monitoring strategy was introduced,which would make the algorithm jump out of the local optimum by re-initializing the positions of some slime mould individuals.With 23 test functions selected,the proposed MSISMA was tested and compared with Equilibrium Slime Mould Algorithm(ESMA),Slime Mould Algorithm combined to Adaptive Guided Differential Evolution Algorithm(SMA-AGDE),SMA,Marine Predators Algorithm(MPA)and Equilibrium Optimizer(EO).Moreover,the Wilcoxon rank-sum test was performed on the running results of all algorithms.Compared with the above algorithms,MSISMA achieves the best average value on 19 test functions and the best standard deviation on 12 test functions,and has the optimization accuracy improved by 23.39%to 55.97%on average.Experimental results show that the convergence speed,solution accuracy and robustness of MSISMA are significantly better.