New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification

As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.

A Clustering Method Based on Brain Storm Optimization Algorithm

In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.

Fuzzy least brain storm optimization and entropy-based Euclidean distance for multimodal vein-based recognition system

Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

基于BSO-BP的船舶油耗预测模型

为解决基于传统反向传播(back propagation,BP)神经网络的船舶油耗预测模型易陷入极小值和误差较大的问题,提出一种利用头脑风暴优化(brain storm optimization,BSO)算法优化BP神经网络的船舶油耗预测模型(简称BSO-BP模型)。以“维多利亚凯娅”号内河游船为研究对象,将BSO-BP模型的预测结果与采用传统BP神经网络以及模拟退火(simulated annealing,SA)算法、遗传算法(genetic algorithm,GA)、粒子群优化(particle swarm optimization,PSO)算法优化的BP神经网络的船舶油耗预测模型的预测结果进行对比分析。结果表明:与传统BP神经网络模型的预测结果相比,BSO-BP模型预测结果的可决系数R^(2)提高了0.003 9,均方误差、均方根误差、平均相对误差、平均绝对误差分别降低了0.034 4、0.154 1、0.010 2、0.017 8,说明在船舶油耗预测中BSO算法对BP神经网络的预测精度有显著的提升作用;BSO-BP模型预测结果的各项评价指标在所对比的5种模型中均表现最好,说明与SA算法、GA和PSO算法相比,BSO算法对BP神经网络的提升效果更好。

基于时延Petri网与BSO的铝挤压线排产调度优化

针对工业生产中铝挤压生产线存在的工序繁杂、排产量大等导致的生产工期较长、效率低等问题,建立了铝挤压生产线的时延Petri网(timed Petri net,TdPN)模型并进行合理性分析;将头脑风暴优化算法(brain storm optimization,BSO)引入TdPN模型,提出了基于变迁序列个体编码解码方式的铝挤压排产调度问题优化调度算法。算法中采用模拟退火局部搜索机制改善BSO算法在后期的寻优性能,实现最小化批次完工时间的排产调度目标;仿真结果表明该方法能够缩短生产线排产工期提高生产效率,为工业生产排产调度问题提供了新的解决方法。

基于分区搜索和强化学习的多模态多目标头脑风暴优化算法

维持种群多样性和提高算法搜索效率是多模态多目标优化亟需解决的两大问题。为解决以上问题,提出了一种基于分区搜索和强化学习的多模态多目标头脑风暴优化算法(MMBSO-ZSRL)。在MMBSO-ZSRL中,首先将决策空间分解为多个子空间以降低搜索难度和维持种群多样性;然后,使用SARSA(state-action-reward-state-action)算法来平衡头脑风暴算法的全局探索和局部开发能力;并使用特殊拥挤距离来挑选个体来指导种群进化。为了验证所提算法的性能,选取六种先进的多模态多目标优化算法来进行比较,并选取IEEE CEC2019多模态多目标问题基准测试集来对所有比较算法的性能进行测试。实验结果表明,MMBSO-ZSRL的整体性能要显著优于其他六种比较算法。MMBSO-ZSRL不仅可以找到多样性和逼近性更好的帕累托前沿,而且可以在决策空间找到更多的帕累托最优解。

群体智能算法在路面参数反分析中的适用性及优选策略

随着群体智能算法在路面参数反演中的成功应用,复杂多元非线性优化难题得以解决,但算法的选择仍然是路面参数反分析问题中亟待解决的难题。针对路面参数反分析中模型复杂、反演参数众多、绝大多数运算时间消耗在正算程序上等问题,选择8种常见的群体智能算法,开展限定正算调用次数下算法性能相关研究,并以考虑材料横观各向同性以及层间接触状态的路面结构参数反演问题为例,对群体智能算法进行实际测试。结果表明:不同算法各具特点,其中,粒子群算法、遗传算法、头脑风暴算法、人工蜂群算法以及烟花算法在多峰问题上具有较好的适用性;萤火虫算法在解决最优解附近存在平缓区域的问题时具有较快的收敛速度;对于遗传算法,实数编码方式后期收敛速度较二进制编码方式有所提高,但对于多峰问题的搜索能力有所下降;鱼群算法、混合蛙跳算法仅有在较大正算调用次数下才有较好的寻优能力。对于路面参数反演问题,从弯沉曲线匹配上看,粒子群算法、遗传算法、头脑风暴算法以及萤火虫算法均有较好的反演结果;而从相关系数上看,头脑风暴算法具有最佳反演结果。

基于BSO算法的TA7电火花成形加工多目标优化

针对现有电火花机床上没有钛合金加工电规准的问题,设计中心组合实验,综合考量空载电压、峰值电流、脉冲宽度以及脉冲间隔对TA7电火花加工材料去除率、表面粗糙度和电极损耗率的影响。建立材料去除率、表面粗糙度和电极损耗率的预测模型。为了同时兼顾材料去除率、表面粗糙度和电极损耗率这三个相互矛盾的工艺目标,建立工艺目标优化模型,设计头脑风暴算法对优化模型进行求解。对优化结果进行验证:材料去除率、表面粗糙度和电极损耗率的实测值与优化结果的平均相对误差分别为5%、4.7%、5.5%。验证结果表明头脑风暴算法可以有效的获得TA7钛合金电火花加工的最佳工艺参数。

基于BSO-SVR的香蕉遥感时序估产模型研究

为了提高有限样本下遥感时序估产效果,本文提出一种基于BSO-SVR的香蕉遥感时序估产模型。该模型以广西壮族自治区扶绥县的71块香蕉田块为研究区,利用时间序列Sentinel-2遥感影像数据,结合实测产量数据,对2019—2020年香蕉产量进行预测与分析。融合阈值分割和形态学开操作方法,滤除香蕉关键生育期内遥感影像的厚云和云阴影区域;引入头脑风暴优化算法(Brain storming optimization algorithm,BSO)自动搜寻支持向量回归算法(Support vector regression,SVR)的最优惩罚因子和核函数参数,解决SVR模型的参数优化不足导致模型预测精度低的问题;搭建基于BSO-SVR的时间序列遥感估产模型,深入挖掘多时相遥感信息,以提升香蕉估产准确度。结果表明,相较于网格搜索算法(Grid search,GS)和灰狼优化算法(Grey wolf optimizer,GWO)搜寻SVR模型的最优参数,本文提出的头脑风暴优化算法具有更高的预测精度和更快的预测速度,在2019年和2020年BSO-SVR模型测试集的决定系数(Coefficient of determination,R;)分别为0.777和0.793,验证集R;分别为0.765和0.636,运行时间分别为0.320、0.331 s;与传统的岭回归模型(Ridge regression,RR)和偏最小二乘回归模型(Partial least squares regression,PLSR)相比,BSO-SVR模型的预测性能最佳,其次是RR模型,PLSR模型表现最差。本文提出的时序估产模型实现了香蕉田块产量的精准预估。

低碳视角下改进DMBSO算法的垃圾收运路径优化

为解决“垃圾围城”难题,设计改进基于讨论机制的头脑风暴优化(discussion mechanism based brain storm optimization,DMBSO)算法对垃圾收运路径进行优化。首先,在低碳环保背景下,考虑车辆载重对碳排放量的影响,建立了以最短路径和极小化碳排放量为目标的绿色垃圾收运路径优化模型;然后设计改进DMBSO算法,其包含组间讨论和组内讨论,使算法在全局搜索和局部搜索达到平衡,并且引入逆转算子、启发式交叉算子和精英保留策略,使改进DMBSO算法适用于解决离散组合优化问题;最后对9个标准算例和1个实例进行仿真,结果表明改进DMBSO算法优于头脑风暴优化(brain storm optimization,BSO)算法和蚁群算法(ant colony optimization,ACO)算法,能够同时降低运输距离和碳排放量,具有很好的收敛效果和鲁棒性。

DBBSO算法在低空航线规划中的应用

为了实现低空范围内的航线规划,首先通过转换坐标系,将多元问题转换为求各个航迹点纵坐标的单元问题,然后在综合考虑航迹段长度和威胁环境的约束条件下,通过建立两个约束条件的数学模型,最终确立头脑风暴算法(BSO)的目标函数,进而求取最优解,实现低空范围内的航线规划。在此基础上对传统头脑风暴算法进行改进,用密度聚类(DB scan)算法替代了k均值聚类(k means)算法。仿真实验证明,改进后的算法较传统算法寻优能力更强,最终得到的航迹段更短,更适用于航线规划。

面向非线性方程组的学习型头脑风暴优化算法

求解非线性方程组的难点是在一次运行中获取问题的多个根,常规求解方法难以同时满足解的精度和解的数量要求。提出一种基于知识学习的目标空间头脑风暴优化(LBSOOS)算法,通过将非线性方程组问题建模为多模态优化问题进行求解,在求解过程融合算法的求解特性和待求解问题的领域知识,采用求解问题学习和求解算法学习两种学习方式解决求解精度和解集合多样性的冲突。从算法层面改进算子的学习方式,将随机解的扰动算子替换为最差解的解间学习,提高算法的整体寻优能力。通过对多模态问题进行分析,在算法中增加额外的档案集,保证输出解集合的多样性。将LBSOOS算法与5种群体智能优化算法在7个非线性方程组问题上进行性能测试,实验结果表明,LBSOOS算法在保证求解精度的条件下,在绝大多数测试问题上的求解多样性优于BSO、BSOOS、PIO等对比算法。

基于证据推理的应急医疗物资配送优化研究

为减轻突发公共卫生事件对灾民的二次伤害,针对初期应急医疗物资供不应求的情况,采用证据理论结合优劣解距离法(TOPSIS)计算需求紧急度,确定医疗点的实际物资分配量和风险等级,以高风险区优先配送且总配送成本最小化为目标,建立考虑时间窗的应急医疗物资配送模型,并应用头脑风暴算法(BSO)进行求解,通过算例验证模型的可行性和算法的有效性。结果表明,相较于传统配送模型,考虑需求紧急度的应急医疗物资配送模型可以保证所有高风险区优先配送,同时降低成本5.79%。证明该模型可以有效改善应急配送的盲目性,兼顾应急医疗物资配置的公平、效率和成本。

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

Steel dome structures,with their striking structural forms,take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns.In this paper,the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider(WS),grey wolf(GW),and brain storm optimization(BSO).The structural elements of the domes are treated as design variables collected in member groups.The structural stress and stability limitations are enforced by ASD-AISC provisions.Also,the displacement restrictions are considered in design procedure.The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface(OAPI).The optimum spatial steel dome designs achieved by proposed WS,GW,and BSO algorithms are compared with respect to solution accuracy,convergence rates,and reliability,utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization(HBO)algorithm.

带时间窗的多中心半开放式VRPSDP问题研究

针对带时间窗的多中心半开放式同时送取货车辆路径问题,构建了配送中心车辆进出平衡且以车辆配送距离最小化为目标的带时间窗的多中心半开放式同时送取货车辆路径问题的数学模型。设计了混沌变异头脑风暴算法求解该问题,采用顺序交叉策略增加种群多样性,设置2种混沌映射进行混沌变异操作,利用混沌变异的多样性、遍历性和随机性,增强算法全局搜索能力。通过多组算例对比,不仅验证所提算法求解多种车辆路径问题的有效性与稳定性,还验证了带时间窗下的多中心半开放同时送取货配送模式优于多中心闭合式同时送取货配送模式。研究成果不仅拓展了车辆路径类的模型,还为相关物流企业提供一种决策参考。

基于头脑风暴优化算法的多机器人气味源定位

针对现有室内湍流环境下多机器人气味源搜索算法存在历史浓度信息利用率不高、缺少调节全局与局部搜索的机制等问题,提出头脑风暴优化(BSO)算法与逆风搜索结合的多机器人协同搜索算法。首先,将机器人已搜索位置初始化为个体,以机器人位置为中心聚类,有效利用了历史信息的指引作用;然后,将逆风搜索作为个体变异操作,动态调节选中一个类中个体或两个类中个体融合生成新个体的数量,有效调节了全局和局部搜索方式;最后,根据浓度和持久性两个指标对气味源进行确认。在有障碍和无障碍两个环境中将所提算法与三种群体智能多机器人气味源定位算法进行定位对比仿真实验,实验结果表明,所提算法的平均搜索时间减少33%以上,且定位准确率达到100%。该算法能够有效调节机器人全局和局部搜索关系,快速准确定位气味源。

基于讨论机制的头脑风暴优化算法

为了克服头脑风暴优化(BSO)算法易陷入局部最优导致早熟收敛的问题,提出新型的基于讨论机制的头脑风暴优化(DMBSO)算法.该算法运用组内讨论和组间讨论这一新机制取代BSO算法中的个体更新过程,分别控制算法的全局搜索和局部搜索能力.通过线性递减和线性递增方式调整组间讨论和组内讨论次数,使算法搜索初期加强全局搜索能力,搜索后期加强局部细致搜索能力,有效地防止早熟问题.对6个经典测试函数(BFs)的10维、20维、30维问题分别进行测试来评估DMBSO的效果.结果表明,DMBSO算法与BSO算法和经典的粒子群(PSO)算法相比,可以有效地避免陷入局部最优,稳定地找到更好的最优值,而且随着问题维度的增加,DMBSO表现出更强的鲁棒性.

目标空间聚类的差分头脑风暴优化算法

作为一种新型的群体智能优化算法,头脑风暴优化(brain storm optimization,BSO)算法一经提出便引起了众多研究者的关注.本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上,提出了基于目标空间聚类的差分头脑风暴(difference brain storm optimization based on clustering in objective space,DBSO–OS)算法.算法通过对目标空间的聚类替代对决策空间的聚类,减小了算法的运算复杂度;采用差分变异代替高斯变异来增加种群的多样性.多个测试函数的仿真结果表明,目标空间聚类的差分头脑风暴算法不仅提高了算法的寻优速度,而且提高了算法的寻优精度.文中进一步分析了参数对算法性能的影响,设计了最佳参数选择方案,并用于对实际热电联供经济调度问题的求解,验证了算法的实用性.

高维多目标头脑风暴优化算法

多目标优化的两个核心指标是收敛性和多样性,而对二者加以优化和权衡是多目标进化算法的关键.头脑风暴优化算法作为一种新型的群体智能优化算法,一经提出便引起了众多研究者的关注.本文在对现有的多目标头脑风暴优化算法研究的基础上,通过对决策变量进行分析,围绕收敛性和多样性分别进行优化,在对收敛性优化时通过分解策略增加选择压力,而在对多样性优化时以参考点更新种群增加多样性,最终扩展并提出了高维多目标头脑风暴优化算法.此外,本文提出一种以角点为聚类中心的自适应聚类方式,明确个体的导向,提高种群的扩展性.与现有的几种效果较好的多目标进化算法进行比较,大量的仿真结果表明了本文的算法具有优秀的性能.