An Improved Elite Slime Mould Algorithm for Engineering Design

The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements.As a representative,Slime mould algorithm(SMA)is widely used because of its superior initial performance.Therefore,this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems.For this aim,the structure of SMA is adjusted to develop the efficiency of the original method.As a stochastic optimizer,SMA mainly stimulates the behavior of slime mold in nature.For the harmony of the exploration and exploitation of SMA,the paper proposed an enhanced algorithm of SMA called ECSMA,in which two mechanisms are embedded into the structure:elite strategy,and chaotic stochastic strategy.The details of the original SMA and the two introduced strategies are given in this paper.Then,the advantages of the improved SMA through mechanism comparison,balance-diversity analysis,and contrasts with other counterparts are validated.The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA.Also,SMA is applied to four structural design issues of the welded beam design problem,PV design problem,I-beam design problem,and cantilever beam design problem with excellent results.

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality.With the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic management.Traffic Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data.Neural Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time.Deep Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction tasks.With this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart cities.Initially,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization approach.Besides,BiGRUmodel is employed for effective forecasting of traffic in smart cities.Moreover,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method.The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

IBMSMA: An Indicator-based Multi-swarm Slime Mould Algorithm for Multi-objective Truss Optimization Problems

This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity;the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution;and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.

Boosting Kernel Search Optimizer with Slime Mould Foraging Behavior for Combined Economic Emission Dispatch Problems

Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the combined economic emission dispatch(CEED)problem.Inspired by the foraging behavior in the slime mould algorithm(SMA),the kernel matrix of the kernel search optimizer(KSO)is intensified.The proposed BKSO is superior to the standard KSO in terms of exploitation ability,robustness,and convergence rate.The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms.BKSO performs better in statistical results and convergence curves.At the same time,BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases,and the Pareto solution obtained is also better than other MAs.Based on the experimental results,BKSO has better performance than other comparable MAs and can provide more economical,robust,and cleaner solutions to CEED problems.

S-RRT path planning based on slime mould biological model

To improve the security and effectiveness of mobile robot path planning, a slime mould rapid-expansion random tree(S-RRT) algorithm is proposed. This path planning algorithm is designed based on a biological optimization model and a rapid-expansion random tree(RRT) algorithm. S-RRT algorithm can use the function of optimal direction to constrain the generation of a new node. By controlling the generation direction of the new node, an optimized path can be achieved. Thus, the path oscillation is reduced and the planning time is shortened. It is proved that S-RRT algorithm overcomes the limitation of paths zigzag of RRT algorithm through theoretical analysis. Experiments show that S-RRT algorithm is superior to RRT algorithm in terms of safety and efficiency.

Bio-Imitation of Mexican Migration Routes to the USA with Slime Mould on 3D Terrains

Plasmodium ofPhysarum polycephalum （P. polycephalum） is a large single cell visible by an unaided eye. It shows sophisticated behavioural traits in foraging for nutrients and developing an optimal transport network of protoplasmic tubes spanning sources of nutrients. When placed in an environment with distributed sources of nutrients the cell computes＇ an optimal graph spanning the nutrients by growing a network of protoplasmic tubes. P. polycephalum imitates development ofman-made transport networks of a country when configuration of nutrients represents major urban areas, We employed this feature of the slime mould to imitate mexican migration to USA. The Mexican migration to USA is the World＇s larger migration system. We bio-physically imitated the migration using slime mould P. poIycephalum. In laboratory experiments with 3D Nylon terrains of USA we imitated development of migratory routes from Mexico-USA border to ten urban areas with high concentration of Mexican migrants. From results of laboratory experiments we extracted topologies of migratory routes, and highlighted a role of elevations in shaping the human movement networks.

Slime mould imitates transport networks in China

Purpose-The purpose of this paper is to study the slime mould Physarum polycephalum as an ideal biological substrate for transport networks.When presented with several sources of nutrients the slime mould propagates colonises the sources and spans them with a network of protoplasmic tubes allegedly optimised for transfer of nutrients and metabolites.Such formation of slime mould’s protoplasmic network resembles development of man-made transport systems.Thus,it sounds reasonable to compare the protoplasmic network with an established network of vehicular transport links to uncover potential(dis-)similarities between slime mould grown and man-made networks and shed more light onto general principle guiding growing biological and socio-engineering systems.Design/methodology/approach-The paper proceeds by representing major urban areas of China by oat flakes,inoculating the slime mould in Beijing,waiting till the slime mould colonises all urban areas,or colonises some and cease further propagation,and analysing the protoplasmic networks formed and comparing with man-made motorway network and planar proximity graphs.Findings-Laboratory experiments found that P.polycephalum provides a very good match for the Chinese motorway networks.Moreover,both the Chinese motorway network and the slime mould protoplasmic networks have minimum spanning trees and other proximity graphs as their sub-graphs.The experiments also identified the urban areas unlikely to be spanned by the protoplasmic networks,which may reflect hot-spots in existing challenges of modernising the motorways.Originality/value-The paper demonstrated the strong component of transport system built by slime mould of P.polycephalum on major urban areas of China consisting of one chain of four nodes and one planar graph with three leaves and eight cycles;the planar graph resides on the urban areas in the south-east part of China.

基于改进黏菌算法的光伏多峰值MPPT控制

针对传统最大功率点跟踪技术在局部遮阴等天气条件下存在无法追踪到全局最大功率点的问题,提出一种基于改进黏菌算法的MPPT控制。首先,对太阳电池模型及多峰值特性进行分析;其次,在黏菌算法中引入领导者策略和基于最优个体的凸透镜反向学习策略,在提高算法计算精度、收敛速度的同时克服了算法易“早熟”现象;最后,根据光伏阵列最大功率输出特性分别确定算法优化模型、初始化位置及重启机制。仿真与实验结果表明:基于改进黏菌算法的MPPT控制能快速、准确地跟踪到全局最大功率点,有效规避陷入局部最优问题,提高了光伏系统的转换效率。

基于改进黏菌算法的移动机器人路径规划

针对标准黏菌算法(Slime Mould Algorithm,SMA)收敛速度较慢、初始种群多样性低以及全局寻优较差等问题,提出一种融合多策略的黏菌算法(Multi-strategy improved Slime Mould Algorithm,MSMA)。在黏菌初始种群中利用Tent混沌映射产生的混沌序列,丰富算法种群多样性,避免局部最优;在黏菌接近食物阶段引入莱维飞行策略,改进黏菌的寻优方式;利用黄金正弦算法的遍历性,提高黏菌算法的搜索精度。将MSMA应用在移动机器人的全局路径规划上,获取路径规划结果。所提算法具有较好的收敛性以及全局搜索的能力,在移动机器人路径规划方面具有较好的规划性能和寻优能力。

相空间重构与改进SMA优化SVR的网络流量预测

为提高网络流量预测精度,提出结合相空间重构与改进黏菌优化支持向量回归的预测模型。为解决黏菌算法收敛慢、易得局部最优的不足,引入3种形态对立学习对种群进行初始化,提高种群多样性;利用非线性反馈因子更新机制,均衡全局搜索与局部开发;设计柯西-高斯混合变异对最优解变异,扩展搜索空间,避免陷入局部最优。利用改进黏菌算法对支持向量回归优化调参,有效解决超参初值敏感缺陷,提高学习精度和收敛速度,以此构建网络流量预测模型。实验结果表明,改进模型预测误差更小,能够实现高精度和实时性预测要求。

战场环境下遗传黏菌算法的多机协同任务分配

针对已知战场环境下的多无人机协同任务分配问题,提出基于融合遗传黏菌算法的任务分配方法.综合单机约束、机群总体收益和损耗以及任务需求等条件,构建多机协同任务分配目标函数.针对遗传算法易陷入局部最优、黏菌算法收敛慢的问题,改进遗传迭代和黏菌探索行为.将离散黏菌算法引入遗传算法,增强融合算法的搜索能力.在种群迭代中加入干扰操作,提高求解精度.在已知环境下进行分配试验和路径演示,并与其他算法进行对比.结果表明,利用所提出的融合算法,能够获得目标函数值更高的任务分配方案.

融合动态权重系数与Levy飞行的黏菌优化算法

为了克服黏菌算法在后期收敛速度缓慢、容易陷入局部极值的缺点,提出一种融合动态权重系数与Levy飞行的黏菌优化算法.首先利用Bernoulli混沌映射对黏菌种群进行初始化,可大幅度提高种群多样性;提出动态权重系数策略,即在位置更新公式中引入一个非线性变化的权重系数ω,动态调整搜索步长,均衡算法的全局搜索能力;借助Levy飞行策略,增强算法后期局部探索能力,加快收敛速度.文中选用10个通用测试函数对改进黏菌算法与其他五种算法进行比较,实验结果显示,改进后的黏菌算法在单峰函数和多峰函数上的寻优效率更高,寻优效果理想,全局搜索和局部开发能力更平衡.

双碳目标下我国电动汽车碳减排贡献潜力分析

为分析碳达峰背景下我国电动汽车行驶阶段碳减排潜力,对电动汽车碳排放进行了多情景测算分析。首先,构建PCA-SMA-LSSVM预测模型对我国电动汽车保有量进行有效预测;然后,考虑电动汽车发展、电源及电网结构优化多种情景组合,对未来电动汽车碳排放潜力进行测算分析。最后,基于测算结果提出激发电动汽车碳减排潜力的相关建议。

基于WT和黏菌算法的LSSVM短期风功率预测

针对风电出力存在随机性、波动性等问题,建立基于小波变换(WT)与黏菌算法(Slime mould algorithm, SMA)优化最小二乘支持向量机(LSSVM)关键参数的风功率预测模型。首先利用小波变换将风功率信号以及风速信号分解为多个不同频率的平稳的子序列,并提出采用一种黏菌优化算法优化LSSVM的参数,同时引入气象因素,包括风速、风向、温度、气压、湿度作为输入,分别建立模型来预测风电功率。通过将各个模型预测结果加和得到完整的风功率预测值。使用某风电厂数据进行仿真验证,实验结果表明,所提出的WT-SMA-LSSVM预测模型在短期风功率预测中具有更高的预测精度。

基于改进黏菌算法的特征选择方法

[目的]在经常处理高维数据集的大数据时代中,特征选择是至关重要的.黏菌算法(slime mould algorithm,SMA)因其简单高效而被广泛应用于特征选择领域,并得到改进.然而,现有改进大多局限于策略添加和算法混合,未根据特征选择问题的特点进行深入改进.为此,本文提出了一种基于改进SMA(improved SMA,ISMA)的特征选择方法.[方法]首先,针对在特征选择时适应度函数值域较小导致的SMA全局探索和局部开发能力不平衡的问题,修改决定黏菌位置更新方式的参数;其次,针对SMA倾向往原点方向收敛的问题,改进SMA的位置更新公式;最后,针对SMA容易陷入局部最优的问题,提出一种基于均衡池改进黏菌位置更新公式的方法.进一步选取Musk1数据集和Lymphography数据集对比ISMA和SMA的全局探索和局部开发能力,并选取11个UCI数据集评价ISMA的性能.[结果]与SMA相比,ISMA具有更强的全局探索能力和局部开发能力,能够很好地平衡探索与开发.与SMA、GA和BGWO1等8种算法相比,ISMA在提高模型分类性能和降低特征维度上均有一定的竞争力.从平均分类准确率的角度看,与SMA相比,ISMA在所有数据集上均优于SMA,平均分类准确率最高提升6.53个百分点.与其他对比算法相比,ISMA在9个数据集上取得最优的平均分类准确率,而在剩下的2个数据集上也取得了次优的平均分类准确率,与第一名仅分别相差0.19个百分点和0.05个百分点,同时其平均维度缩减率均优于第一名.从平均维度缩减率的角度看,ISMA在2个数据集上取得最优的维度缩减率,总体表现良好.[结论]本文提出的基于ISMA的特征选择方法具有更高的泛化性能,与其他元启发式特征选择算法相比也有一定的优势.

基于TVFEMD和多模型融合的变压器油中溶解气体体积分数预测方法

油中溶解气体分析可以反映变压器的运行状态,对其体积分数精准预测可以为变压器早期故障判别和预警提供理论支撑。为此提出了一种基于时变滤波经验模态分解和多模型融合的变压器油中溶解气体体积分数预测方法。首先,通过时变滤波经验模态分解将气体体积分数序列分解为多个子序列,降低其非平稳性;其次,利用多模型融合策略,将4种不同单模型的预测结果进行融合重构,因单模型权重系数对预测结果有显著影响,利用改进黏菌算法对权重系数进行优化,以提高预测精度;最后,通过算例验证表明,相比于传统的预测模型,所提方法具有更高的预测精度,可以更好地预测油中气体体积分数的变化趋势。

融合多策略的改进黏菌算法及工程应用

黏菌优化算法(Slime Mould Algorithm, SMA)是根据黏菌个体振荡捕食行为提出的一种新型元启发式算法,因其原理简单被应用于多种复杂的优化问题中,基本的SMA在处理一些较为复杂的问题时仍然存在收敛速度较慢、精度不足、鲁棒性差等劣势。为克服这些缺点,提升原算法性能,提出一种融合Sine混沌映射、t分布以及黄金正弦策略的改进黏菌算法(GTSMA)。首先,引入Sine混沌序列初始化种群,提高算法在初始迭代过程中黏菌种群个体的多样性;其次,在黏菌个体更新位置过程中将自由度参数t与基本SMA融合,增加算法跳出局部最优的概率;最后,通过与黄金正弦算法相结合,挑选更优秀的黏菌个体,输出最优解。利用基准测试函数、CEC2021测试集将GTSMA与其他算法进行对比,实验结果表明GTSMA在测试过程中鲁棒性、寻优精度和收敛性能都优于其他算法。将GTSMA应用于工程优化问题,进一步验证了GTSMA在处理实际优化问题上的优越性。

一种旋转机械综合故障检测和模式识别模型

针对传统故障诊断方法只关注故障检测部分,而对样本是否存在故障的研究较少的问题,提出了一种基于自适应噪声完备经验模态分解(CEEMDAN)-注意熵(AE)和黏菌算法优化极限学习机(SMA-ELM)的旋转机械综合故障诊断模型。首先,针对正常样本和故障样本的复杂性差异,建立了注意熵阈值,计算旋转机械的AE,并将其与阈值进行了比较,若熵值小于该阈值则表明样本存在故障,反之样本是健康的;然后,利用CEEMDAN对故障样本的振动信号进行了分解,提取前6阶分量的AE值;最后,将故障特征输入至SMA-ELM模型中进行了故障识别,利用3种旋转机械故障数据集对该综合故障诊断模型的可靠性进行了研究。研究结果表明:该阈值设置方法可以100%准确地检测样本是否存在故障,后续的故障诊断模型能够准确地检测出样本的故障类型,识别准确率分别达到了99.44%、100%和98%。该综合故障诊断模型能够避免正常样本被误判为故障样本,为旋转机械的故障检测提供了一种可行的思路。

基于动态双种群的黏菌和花粉混合算法

针对单一启发式算法易受自身原理导致的全局和局部搜索不平衡的问题,提出了一种基于动态双种群的黏菌和花粉混合算法HASMFP。首先,通过种群个体和当前最优个体之间的距离,将种群动态划分为黏菌子种群和花粉子种群分别进行搜索,以更有效地平衡算法的探索能力和开发能力;其次,对全局搜索融入相似度与适应度的综合排序感知机制来提高黏菌子种群的多样性,以帮助黏菌算法跳出局部最优;最后,在标准花粉算法的全局搜索中加入动态权重和恒定收缩系数,并对局部搜索设计了精英引导项来提高算法的收敛速度和搜索精度。选用CEC2017测试集中的12个函数作为实验测试集,将HASMFP与ISMA、DTSMA、HLFPA、SCFPA和tMFPA五种改进算法进行性能测试对比。还对HASMFP的各个改进策略进行消融实验,实验表明在综合改进策略的共同作用下,HASMFP的优化性能排名第一。基于实验结果的Friedman检验表明,HASMFP能够获取最优的性能。

基于改进黏菌算法优化BiLSTM的短期供热负荷控制预测

针对短期供热负荷控制预测的问题,提出了一种基于改进黏菌算法优化BiLSTM的预测模型。利用猫映射、T分布变异和随机反向学习等改进策略对黏菌算法进行改进,改进后的黏菌算法优化BiLSTM网络参数,构建ISMA-BiLSTM模型,对换热站热负荷进行预测。实验结果表明,ISMA-BiLSTM模型与SMA-BiLSTM、BiLSTM和LSTM模型相比,预测结果更加合理且预测精度有所提高,在短期供热负荷预测中能满足实际工程控制需要。