Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is based on human behavior in which people gain and share their knowledge with others. Different types of stochastic fractional programming problems are considered in this study. The augmented Lagrangian method (ALM) is used to handle these constrained optimization problems by converting them into unconstrained optimization problems. Three examples from the literature are considered and transformed into their deterministic form using the chance-constrained technique. The transformed problems are solved using GSK algorithm and the results are compared with eight other state-of-the-art metaheuristic algorithms. The obtained results are also compared with the optimal global solution and the results quoted in the literature. To investigate the performance of the GSK algorithm on a real-world problem, a solid stochastic fixed charge transportation problem is examined, in which the parameters of the problem are considered as random variables. The obtained results show that the GSK algorithm outperforms other algorithms in terms of convergence, robustness, computational time, and quality of obtained solutions.     

Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.     

一类求解带随机成本的生产运输问题的线性逼近方法

生产和运输集成计划问题在许多工业工程领域都普遍存在。要给出最优的生产和运输计划就必须考虑实际工业管理过程中存在的不确定性因素。本文研究了生产厂家的生产能力、商家的需求量和单位运输成本等因素为随机变量情况下的产品生产与运输成本问题,建立了该类问题的随机优化模型。在一定的假设条件下,推导了所建模型的确定等价类。基于问题的结构特征,提出了求解生产和运输计划的一种线性逼近方法,数值例子表明该种方法的应用前景。     

基于启发式算法的乘用车物流运输问题分析

伴随着汽车工业的高速崛起,乘用车物流运输问题也快速走进人们的视野。由于现在很多物流公司在制定运输计划时主要依赖调度人员的经验,在面对复杂的运输任务时,往往效率较低且运输成本不尽理想。考虑到影响乘用车物流运输成本的主要因素分别为轿运车的使用数量、轿运车的单价以及行驶里程数等等,本文采用建立逐级目标的模式,应用启发式算法,结合计算机软件,给出了求解乘用车物流运输问题的数学模型。应用此模型求解了2种不同类型的乘用车物流运输问题,提出了合理的运输方案。此项工作对今后物流公司处理此类运输问题提供了重要的参考价值。     

A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric Disorders

A number of brain research projects have recently been carried out to study the etiology and mechanisms of psychiatric disorders. Although psychiatric disorders are part of the brain sciences, psychiatrists still diagnose them based on subjective experience rather than by gaining insights into the pathophysiology of the diseases. Therefore, it is urgent to have a clear understanding of the etiology and pathogenesis of major psychiatric diseases, which can help in the development of effective treatments and interventions. Artificial intelligence (AI)-based applications are being quickly developed for psychiatric research and diagnosis, but there is no systematic review that summarizes their applications. For this reason, this study briefly reviews three main brain observation techniques used to study psychiatric disorders—namely, magnetic resonance imaging (MRI), electroencephalography (EEG), and kinesics diagnoses—along with related AI applications and algorithms. Finally, we discuss the challenges, opportunities, and future study directions of AI-based applications.     

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms are proposed to solve problems in various fields of medical treatment, which is able to reduce the workload of the medical system. Due to excellent learning ability, AI has played an important role in drug development, epidemic forecast, and clinical diagnosis. This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.     

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.     

A Stochastic EM Algorithm for Progressively Censored Data Analysis

Progressive censoring technique is useful in lifetime data analysis. Simple approaches to progressive data analysis are crucial for its widespread adoption by reliability engineers. This study develops an efficient yet easy‐to‐implement framework for analyzing progressively censored data by making use of the stochastic EM algorithm. On the basis of this framework, we develop specific stochastic EM procedures for several popular lifetime models. These procedures are shown to be very simple. We then demonstrate the applicability and efficiency of the stochastic EM algorithm by a fatigue life data set with proper modification and by a progressively censored data set from a life test on hard disk drives. Copyright © 2013 John Wiley & Sons, Ltd.     

An interactive approach for the multi-objective transportation problem with interval parameters

The basic transportation problem (TP) deals with the transportation of goods from a set of supply points to a set of demand points so as to minimise linear transportation costs. The multi-objective transportation problem (MOTP) extends TP to take into consideration multiple conflicting objectives such as transportation cost, average delivery time of the commodities, unfulfilled demand and so forth. We develop an interactive approach to determine the preferred compromise solution for the MOTP where the coefficients of the objective functions and the source and destination parameters have been expressed as interval values by the decision-maker to deal with the impreciseness in the parameters. The proposed method is applied to a real-world MOTP faced by a leading provider of complete broadband access solutions. Results of the case study indicate that the developed method is a practical technique for solving the MOTP with interval parameters.     

随机网络瓶颈容量扩张的期望值模型

在不确定的环境下,怎样去增加一组边的容量到一个指定的瓶颈容量,以至于网络瓶颈扩张的费用最小。假定每一条边的单位扩张费用形是一个随机的变量,它服从正态分布。带有随机单位扩张费用W的网络瓶颈客量扩张问题可以根据一些概率统计规则,列出它的期望值模型的通用表达式。随后,网络瓶颈容量算法、随机模拟方法和遗传算法将合成在一起,设计出该问题的混合智能算法。最后。给出数值案例。     

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning (ML) has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls. ML is a massive area within artificial intelligence (AI) that focuses on obtaining valuable information out of data, explaining why ML has often been related to stats and data science. An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design. The algorithm is designed, depending on the hybrid between the Sine Cosine Algorithm (SCA) and the Grey Wolf Optimizer (GWO), to train neural network-based Multilayer Perceptron (MLP). The proposed optimization algorithm is a practical, versatile, and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna. The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test. It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’ accuracy.     

大型运动会专用道设置的交通规划模型

大型运动会要求主办方在规定时间内将指定人员从运动员村运送到指定比赛场馆.为满足运送时间的要求,通常采用设置专用通道的方法.在满足运送时间的条件下,需要最小化设置专用通道的总成本.提出一个新的交通问题:大型运动会专用道设置的动态交通规划问题.本文为该问题建立了能反映实际问题的数学规划模型.该模型是一个整数非线性规划模型.通过对非线性模型的线性化,可以得到一个整数线性规划模型,并通过数学规划软件求解该线性模型.以广州亚运会为例,详细介绍并分析了对于该问题的建模与求解过程.     

模糊人工蜂群算法的置换流水车间调度问题求解

针对传统人工蜂群算法早熟收敛问题,基于模糊化处理和蜂群寻优的特点,提出一种模糊人工蜂群算法.将模糊输入输出机制引入到算法中来保持蜜源访问概率的动态更新.根据算法计算过程中的不同阶段对蜜源访问概率有效调整,避免算法陷入局部极值.通过对置换流水车间调度问题的仿真实验和与其他算法的比较,表明本算法可行有效,有良好的鲁棒性.     

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.     

不确定车数的随机车辆路径问题模型及算法

研究了一类有时间约束、车辆数量不确定的随机车辆路径问题;建立了该类问题的随机规划数学模型;设计了模型求解的遗传算法、禁忌搜索算法和遗传-禁忌混合算法.禁忌算法采用了对当前解的车辆-顾客分配结构和解的路径顺序分别禁忌的双层禁忌算法,使算法全局性更好,同时也降低了搜索时间.把禁忌算法作为变异算子应用于遗传算法形成了混合算法.最后给出了计算示例,对算法进行了比较分析.     

基于离散差分进化算法的随机车辆路径问题

针对差分进化算法求解组合优化问题存在的局限性,引入计算机语言中的2种按位运算符,对差分进化算法的变异算子进行重新设计,用来求解不确定需求和旅行时间下同时取货和送货的随机车辆路径问题（SVRPSPD）。通过对车辆路径问题的benchmark问题和SVRPSPD问题进行路径优化,并同差分进化算法和遗传算法的计算结果进行比较,验证了离散差分进化算法的性能。结果表明,离散差分进化算法在解决复杂的SVRPSPD问题时,具有较好的优化性能,不仅能得到更好的优化结果,而且具有更快的收敛速度。     

求解对称锥互补问题的一种非精确光滑牛顿方法(英文)

本文给出了一种求解对称锥互补问题的非精确光滑牛顿方法,所采用的互补函数是含一个参数且以FB和CHKS为特例的光滑函数.新方法的每步迭代中,都采用非精确牛顿方法求解由原问题产生的子问题.在一定条件下,新算法具有全局收敛和局部超线性收敛的性质.数值试验表明算法对于求解大规模对称锥互补问题是非常有效的.     

基于货损约束的配送系统优化模型

以流通型集配中心为研究背景,研究了配送过程中产生货损的三种作业方式的选择问题,建立了以货损率为约束条件的整数规划随机模型,通过对随机变量进行等价变换,将不确定性问题转化为确定性等价问题,从而方便了问题的求解.