混合蝙蝠算法在包装废弃物逆向物流中的应用

目的为了提高蝙蝠算法(BA)求解包装废弃物逆向物流问题的性能。方法在标准BA算法的基础上提出混合蝙蝠算法(HBA)。首先,构建新型蝙蝠表达式,使BA算法适用于包装废弃物逆向物流问题的求解。其次,引入自适应惯性权重,改造蝙蝠速度更新公式;然后,引入粒子群算法(PSO),对每次迭代中任一随机蝙蝠进行粒子群操作;最后,利用HBA算法对企业实例和标准算例进行仿真测试。结果企业最优回收距离为776.63 km。与遗传算法(GA)、蚁群算法(ACO)和禁忌搜索算法(TS)相比,HBA算法能够求得已知最优解的标准算例个数最多为6个,求得最好解与已知最优解的平均误差最小为8.58%,平均运行时间最短为4.39s。结论 HBA算法的全局寻优能力、稳定性和运行速度均优于GA算法、ACO算法和TS算法。     

混合蝙蝠算法在包装件配送中的应用研究

目的使蝙蝠算法(BA)适应包装件配送车辆路径问题(VRP)的求解,并提高该算法的求解性能。方法在标准BA算法的基础上提出混合蝙蝠算法(HBA)。首先,设计改进的蝙蝠算法(IBA),使其能够适用于包装件配送VRP问题的求解。其次,引入混沌系统,对IBA算法进行混沌初始化。然后,设计裂变算子和变异算子。在IBA算法迭代前半段,将蝙蝠种群中较差的一半蝙蝠重新混沌初始化,以提高种群多样性。在IBA算法迭代后半段,对陷入局部最优解的蝙蝠进行鲶鱼扰动。最后,提出HBA算法并对企业实例进行仿真测试。结果 HBA算法求得的最优配送距离为773.01 km,相对于GA算法(781.25 km)和IBA算法(786.04 km)分别节约了8.24 km和13.03 km。结论与IBA算法和GA算法相比,HBA算法求解包装件配送VRP问题的全局优化能力更强、收敛速度更快。     

硅掺杂羟基磷灰石生物活性微粉对人血清白蛋白的吸附特性

对含硅羟基磷灰石(Si-HA)与人血清白蛋白(HSA)的吸附特性及动力学特征进行了研究,计算了表观活化能,用等温吸附曲线进行拟合。结果表明,该吸附属于Langmuir型。Si-HA和HA吸附HSA的表观活化能分别为21.28和35.57 kJ.mol-1,20℃下吸附反应的速率常数分别为1.48和1.34 min-1,饱和吸附量分别为25.34和21.34 mg.g-1,Si-HA具有比纯HA更好的吸附优势。通过XRD、FTIR及荧光光谱分析,探讨了HSA在Si-HA和HA表面的吸附作用机制,表明Si-HA与蛋白表面吸附反应是以化学吸附为主、包含物理吸附的混合吸附过程。从分子结构的角度揭示了Si-HA和HA吸附蛋白质性能的差异,为进一步探讨Si-HA和HA生物相容性提供了新途径。     

基于PSO-BP和GA-BP神经网络再生砖骨料混凝土强度模型的对比研究

采用两种混合算法人工神经网络模型(PSO-BP和GA-BP)预测具有不同砖骨料替代率的再生砖骨料混凝土(RBAC)的抗压强度.以RBAC的水泥质量、水灰比、碎瓷砖(CT 0—5,CT 5—32.5)替代率、碎砖(CB 0—5,CB 5—32.5)替代率及天然骨料(NA 0—5,NA 5—32.5)替代率等八个参数作为混合神经网络模型的输入参数,28 d立方体抗压强度作为输出参数.使用均方根误差(RMSE)、相关系数(R)和平均误差率对两种模型进行验证和对比分析.结果表明,PSO-BP模型与GA-BP模型都能实现高精度的预测,具有强大的泛化能力,总体而言,PSO-BP模型稍好于GA-BP模型,且都优于BP模型.同时,这也证明提出的混合算法神经网络有助于寻找最佳的RBAC配合比设计,提高实验效率.     

基于Dijkstra-蚁群算法的泊车系统路径规划研究

针对智能停车库中自动导引运输车(automated guided vehicle,AGV)存取车路径规划问题,提出了一种基于Dijkstra-蚁群算法(Dijkstra-ACO)的泊车系统路径规划方法.首先利用链接可视图法建立环境模型,并在此环境模型下,采用Dijkstra算法规划出AGV的初始路径;其次,通过引入节点随机选择机制、调整信息素更新方式和限定信息素阈值策略等对基本蚁群算法进行优化改进;最后,选用改进的蚁群算法对初始路径进行优化.结果显示:Dijkstra算法和混合算法均能使AGV有效避开障碍物,然后搜索到一条从起点到终点的无碰优化路径;与Dijkstra算法相比,混合算法能有效提高路径搜索效率,缩短搜索路径长度,改善搜索路径质量,表明该算法正确、可行及有效,且具有较强的全局搜索能力和较好的收敛性能,能够满足AGV存取车路径规划的要求.     

基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。     

基于混合鸡群算法和核极端学习机的锅炉NOx排放的预测

以某300MW亚临界循环流化床锅炉为研究对象,对锅炉的NOx排放量进行预测。利用模拟退火混合鸡群算法(SACSO)和核极端学习机(KELM)对不同工况下NOx的排放量进行建模;对比了差分进化算法,粒子群算法和原始鸡群算法,证明了改进后算法的优越性;之后,又对传统BP算法,支持向量机,极端学习机和核极端学习机模型进行对比;最终确定的SACSO-KELM模型具有更高的预测精度和稳定性以及更好的泛化能力,可选择将此模型用于锅炉NOx排放的建模预测。     

一种基于改进MCMC算法的模型修正方法

标准马尔可夫链蒙特卡罗(MCMC)算法不易收敛、拒绝率高,使其应用受到限制。在贝叶斯方法中引入最大熵值法来估计参数的后验概率密度函数最大值,进而将布谷鸟算法中新鸟巢更新的思想融入Metropolis-Hasting(MH)抽样算法得到改进的MH抽样算法,同时使用支持向量机(SVM)建立待修正参数与有限元模型输出之间的代理模型,以提高模型修正的计算效率。分别使用三自由度线性系统和平面桁架模型来验证本文方法的有效性,结果表明:修正后样本的马尔可夫链混合性能好,停滞概率低,修正后参数相对误差均小于2%。     

进口物资运输的中转站选址-分配问题

结合进口物资的江海联合中转运输,提出了一类带三重能力约束的中转站选址-分配问题(LAP)模型,即在物流网络节点有最大单批通行能力、中转站吞吐能力、运输工具总运输能力三重能力约束下,如何进行中转站选址,并相关资源分配,使总的费用最小.针对该问题构建了混合整数规划模型,提出了一种先进行选址然后进行资源分配的优化算法.还给出了该模型在武汉钢铁(集团)公司进口铁矿石江海联合运输的中转站选址-分配问题的研究实例.     

考虑舒适度的大跨楼盖MTMD系统混合优化设计

围绕大跨楼盖多重调谐质量阻尼器(Multiple Tuned Mass Dampers,MTMD)系统的设计方法开展研究。首先,建立了某高铁站房商业夹层的结构有限元模型,对其动力特性和振动舒适度进行了分析;随后,基于遗传算法(Genetic Algorithm,GA)和模式搜索算法(Pattern Search,PS)构建了混合优化算法A(Hybrid Algorithm A,HA)和混合优化算法B(Hybrid Algorithm B,HB),并基于此建立了大跨楼盖MTMD减振优化设计方法;最后,通过数值算例对比了4种算法的寻优能力和稳定性,并分析了设置MTMD系统后,大跨楼盖加速度响应的分布和衰减规律。研究表明,对于质量、刚度等分布不均匀的复杂大跨楼盖,振动响应规律与一般楼盖有一定的差异;在相近的重分析次数下,利用HA得到的最优值平均为0.653,小于利用GA和PS得到的结果;设置MTMD系统后,大跨楼盖响应较大区域内的节点加速度均有所降低,最大衰减超过了30%。     

Application of the Harmony Search optimization algorithm for the solution of the multiple dam system scheduling

During the last few years Harmony Search Algorithm (HSA), a new optimization technique, has been used effectively in solving large scale problems. In this paper along with a brief presentation of HSA, an application on classic Dam Scheduling problem is presented. This application concerns the optimum operation of a four-reservoir system over 24 hours. The water released from each dam is used for hydropower generation and irrigation. The objective is to maximize the daily benefits gained from the reservoir system over 12 (two-hour) time steps. Two programs were created in order to optimize this particular problem using Visual-Basic and MATLAB respectively. Both programs converged successfully to optimum management, and their main characteristics and results, are presented. The comparison between them reveals some interesting differences regarding their efficiency. The purpose of this paper is to show the potential of HSA and prove its efficiency to optimize complex optimization problems successfully. Major findings of the present paper are the 15 different solutions leading to the same global optimum. These 15 variations of the optimum management practices comprise all possible solutions, as it is proven, and they are detected for the first time ever.     

运输包装优化设计案例分析

目的对运输包装的案例进行分析,使得运输包装的优化设计能达到增强保护性,降低包装综合成本的效果。方法在收集大量运输包装设计案例的基础上,通过对新旧方案的分析比较,归纳总结出结构、材料、尺寸等优化方法,全面覆盖了运输包装优化设计的各个方面。结果根据企业实际案例,提供了不同的优化参数,如产品体积减小了34%,角跌落和面跌落G值减小,单件包装材料成本降低,产品的集合装载率提高了46%等。结论提出"三步走"的运输包装优化设计方法,最终实现了减量、减容、减费、增效的目的,从而降低了包装的综合成本。     

A two-stage path planning approach for multiple car-like robots based on PH curves and a modified harmony search algorithm

In this article, collision-avoidance path planning for multiple car-like robots with variable motion is formulated as a two-stage objective optimization problem minimizing both the total length of all paths and the task’s completion time. Accordingly, a new approach based on Pythagorean Hodograph (PH) curves and Modified Harmony Search algorithm is proposed to solve the two-stage path-planning problem subject to kinematic constraints such as velocity, acceleration, and minimum turning radius. First, a method of path planning based on PH curves for a single robot is proposed. Second, a mathematical model of the two-stage path-planning problem for multiple car-like robots with variable motion subject to kinematic constraints is constructed that the first-stage minimizes the total length of all paths and the second-stage minimizes the task’s completion time. Finally, a modified harmony search algorithm is applied to solve the two-stage optimization problem. A set of experiments demonstrate the effectiveness of the proposed approach.     

A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems

Many global optimization (GO) algorithms have been introduced in recent decades to deal with the Computationally Expensive Black-Box (CEBB) optimization problems. The high number of objective function evaluations, required by conventional GO methods, is prohibitive or at least inconvenient for practical design applications. In this work, a new Kriging–Bat algorithm (K–BA) is introduced for solving CEBB problems with further improved search efficiency and robustness. A Kriging surrogate model (SM) is integrated with the Bat Algorithm (BA) to find the global optimum using substantially reduced number of evaluations of the computationally expensive objective function. The new K–BA algorithm is tested and compared with other well-known GO algorithms, using a set of standard benchmark problems with 2 to 16 design variables, as well as a real-life engineering optimization application, to determine its search capability, efficiency and robustness. Results of the comprehensive tests demonstrated the suitability and superior capability of the new K–BA.     

SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.     

多式联运的最短时间路径-运输费用模型研究

随着经济的迅速发展，单一的运输方式越来越不能满足来自客户的敏捷制造、快速响应市场、物流供应链管理等诸多方面的需求，多式联运为之提供了良好的解决方案；多式联运运输网络考虑了节点间的运输时间、节点处的运输方式转换时间以及可能发生的货运延迟；给出了多式联运下的路径最短时间模型，并根据求得的最短时间路径提供了相应的运输费用模型，为多式联运的相关研究工作提供了理论基础。     

基于货架寿命的乳制品配送车辆路径优化研究

目的 基于货架寿命对库存和配送进行优化研究,降低冷链过程中的乳制品损耗,保证乳制品的质量,降低企业成本,提高消费者的满意度。方法 考虑到货架寿命对配送的影响,在研究货架寿命的基础上,建立乳制品货损率与货架寿命的关系式。在时间窗约束下,以总成本最小为目标,分别构建未考虑货架寿命的配送车辆路径优化模型和考虑货架寿命的配送车辆路径优化模型,并通过改进遗传算法对2种模型进行求解分析。结果 对比2种模型的最优结果,发现在1个配送周期内,基于货架寿命配送的总成本比不考虑货架寿命的运输总成本低3.71%。结论 该研究在一定程度上可以为物流企业减少配送成本,提高企业的经济效益。     

Modified Bat Algorithm for Optimal VM's in Cloud Computing

All task scheduling applications need to ensure that resources are optimally used, performance is enhanced, and costs are minimized. The purpose of this paper is to discuss how to Fitness Calculate Values (FCVs) to provide application software with a reliable solution during the initial stages of load balancing. The cloud computing environment is the subject of this study. It consists of both physical and logical components (most notably cloud infrastructure and cloud storage) (in particular cloud services and cloud platforms). This intricate structure is interconnected to provide services to users and improve the overall system's performance. This case study is one of the most important segments of cloud computing, i.e., Load Balancing. This paper aims to introduce a new approach to balance the load among Virtual Machines (VM's) of the cloud computing environment. The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm (BA). This proposed Modified Bat Algorithm (MBA) allows balancing the load among virtual machines. The proposed algorithm works in two variants: MBA with Overloaded Optimal Virtual Machine (MBA-OOVM) and Modified Bat Algorithm with Balanced Virtual Machine (MBA-BVM). MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.     

一种流程型企业主生产计划优化方法研究

为了解决传统的流程型生产管理方法不适应现代市场需求的问题,提出了一种基于生产柔性提高的主生产计划优化方法.建立了流程型企业的产品需求与生产能力平衡的最优化模型,讨论了模型的求解方法--混合启发式算法.经研究表明,该方法在流程型生产管理理论和实践方面具有一定的参考意义.     

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECG Signal Classification (IBADL-BECGC) approach. To accomplish this, the proposed IBADL-BECGC model initially pre-processes the input signals. Besides, IBADL-BECGC model applies NasNet model to derive the features from test ECG signals. In addition, Improved Bat Algorithm (IBA) is employed to optimally fine-tune the hyperparameters related to NasNet approach. Finally, Extreme Learning Machine (ELM) classification algorithm is executed to perform ECG classification method. The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset. The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.