基于工作流网的应急资源配置与路径规划集成优化

应急管理决策通常包括站点选址、资源配置、运输调度等内容,如何从应急处置整体流程控制的视角对决策内容进行集成建模及优化,是应急管理研究付诸实际应用的关键。本文提出具有资源和不确定时间约束的应急工作流网模型,通过三类库所（状态库所、动作库所、资源库所）及三类时间属性（可视时间、静态时间、动态时间）,揭示多部门联合应急中的作业时序与资源占用关系。在给定整体流程最大完成时间的条件下,以资源消耗与占用成本、资源运输与惩罚成本总和为目标函数,建立应急资源配置与路径规划的集成问题模型,并采用遗传粒子群混合算法对问题进行求解。根据遗传优化得到的应急资源配置方案,借助应急工作流网计算各动作库所、状态库所的时间参数,以此作为约束条件利用嵌套的粒子群算法进行资源运输策略优化。     

Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization

Task Scheduling is a complex combinatorial optimization problem and known to be an NP hard. It is an important challenging issue in multiprocessor computing systems. Discrete Particle Swarm Optimization (DPSO) is a newly developed swarm intelligence technique for solving discrete optimization problems efficiently. In DPSO, each particle should limit its communication with the previous best solution and the best solutions of its neighbors. This learning restriction may reduce the diversity of the algorithm and also the possibility of occurring premature convergence problem. In order to address these issues, the proposed work presents a hybrid version of DPSO which is a combination of DPSO and Cyber Swarm Algorithm (CSA). The efficiency of the proposed algorithm is evaluated based on a set of benchmark instances and the performance criteria such as makespan, mean flow time and reliability cost.     

Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter weights of the Bidirectional Recurrent Neural Networks (BRNN). Furthermore, a time series dataset is tested in the experiments of e-commerce demand forecasting. Finally, the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance (ANOVA) test applied to confirm the performance of the proposed ensemble model. The proposed Algorithm achieved a root mean square error of RMSE (0.0000359), Mean (0.00003593) and Standard Deviation (0.000002162).     

基于改进NSGA-Ⅱ的云制造服务组合优化问题研究

为了实现企业在云制造环境下的节能,提出一种新的云制造服务组合优化方法,该方法既能降低能耗,又能在考虑不确定性的情况下提高服务质量。然后利用双目标模型和改进的NSGA-Ⅱ解决制造服务组合优化问题。实例研究结果表明:该模型从云制造服务平台(CMSP)的角度有效地控制了能耗,改进后的NSGA-Ⅱ在解决该问题上具有与MOPSO和标准的NSGA-Ⅱ相比的准确性和收敛性优势。     

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.     

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.     

Optimization methods for advanced design of aircraft panels: a comparison

Advanced nonlinear analyses developed for estimating structural responses for recent applications for the aerospace industry lead to expensive computational times. However optimization procedures are necessary to quickly provide optimal designs. Several possible optimization methods are available in the literature, based on either local or global approximations, which may or may not include sensitivities (gradient computations), and which may or may not be able to resort to parallelism facilities. In this paper Sequential Convex Programming (SCP), Derivative Free Optimization techniques (DFO), Surrogate Based Optimization (SBO) and Genetic Algorithm (GA) approaches are compared in the design of stiffened aircraft panels with respect to local and global instabilities (buckling and collapse). The computations are carried out with software developed for the European aeronautical industry. The specificities of each optimization method, the results obtained, computational time considerations and their adequacy to the studied problems are discussed.     

Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm

The integration of process planning and scheduling is considered as a critical component in manufacturing systems. In this paper, a multi-objective approach is used to solve the planning and scheduling problem. Three different objectives considered in this work are minimisation of makespan, machining cost and idle time of machines. To solve this integration problem, we propose an improved controlled elitist non-dominated sorting genetic algorithm (NSGA) to take into account the computational intractability of the problem. An illustrative example and five test cases have been taken to demonstrate the capability of the proposed model. The results confirm that the proposed multi-objective optimisation model gives optimal and robust solutions. A comparative study between proposed algorithm, controlled elitist NSGA and NSGA-II show that proposed algorithm significantly reduces scheduling objectives like makespan, cost and idle time, and is computationally more efficient.     

Enhanced Heap-Based Optimizer Algorithm for Solving Team FormationProblem

Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO’s optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.     

Metamodel Assisted Evolutionary Algorithm for Multi-objective Optimization of Non-steady Metal Forming Problems

Multiobjective optimization problems are considered in the field of nonsteady metal forming processes, such as forging or wire drawing. The Pareto optimal front of the problem solution set is calculated by a Genetic Algorithm. In order to reduce the inherent computational cost of such algorithms, a surrogate model is developed and replaces the exact the function simulations. It is based on the Meshless Finite Difference Method and is coupled to the NSGAII Evolutionary Multiobjective Optimization Algorithm, in a way that uses the merit function. This function offers the best way to select new evaluation points: it combines the exploitation of obtained results with the exploration of parameter space. The algorithm is evaluated on a wide range of analytical multiobjective optimization problems, showing the importance to update the metamodel along with the algorithm convergence. The application to metal forming multiobjective optimization problems show both the efficiency of the metamodel based algorithms and the type of practical information that can be derived from a multiobjective approach.     

智能工作流模型及驱动研究

基于工作流参考模型提出了智能工作流模型,并根据该模型给出了一种通用、灵活且适应性强的工作流流程控制方法--弧线控制法。该方法仅需要几种简单的元素(条件集、流向)就可实现不同流程类型的流转,并且可以很容易地被引擎解释及控制;研究了工作流智能处理的模型、原理、方法及实现,提出了任务处理引擎(仲裁)及任务处理信息库,通过仲裁的处理能力及信息库信息的完备性和可扩充性最终实现了工作流的智能性;通过全文检索等技术对自然语言描述的任务选择最佳执行方法并进行自动处理,增强了工作流智能水平。     

Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems

Parallel and distributed systems play an important part in the improvement of high performance computing. In these type of systems task scheduling is a key issue in achieving high performance of the system. In general, task scheduling problems have been shown to be NP-hard. As deterministic techniques consume much time in solving the problem, several heuristic methods are attempted in obtaining optimal solutions. This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Sorting Particle Swarm Optimization Algorithm (NSPSO) to schedule independent tasks in a distributed system comprising of heterogeneous processors. The problem is formulated as a multi-objective optimization problem, aiming to obtain schedules achieving minimum makespan and flowtime. The applied algorithms generate Pareto set of global optimal solutions for the considered multi-objective scheduling problem. The algorithms are validated against a set of benchmark instances and the performance of the algorithms evaluated using standard metrics. Experimental results and performance measures infer that NSGA-II produces quality schedules compared to NSPSO.     

业务流程时间性能定量分析

时间性能的定量分析是保证流程模型有效性和提高业务流程质量的关键.针对管理型流程的特点,基于任务活动执行时间为零或者服从指数分布的假设,建立了业务流程的广义随机工作流网模型,并利用广义随机Petri网和马尔可夫链理论来分析流程的时间性能,获得了流程平均执行时间的计算公式,最后给出了应用验证实例并和仿真结果进行了比较.     

Fused and Modified Evolutionary Optimization of Multiple Intelligent Systems Using ANN,SVM Approaches

The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay or errors in the diagnosis of the disease can lead to delays in the management of plant disease spreading and damage and related material losses. Comparative new heuristic optimization of swarm intelligence, Grasshopper Optimization Algorithm was inspired by grasshopper movements for their feeding strategy. It simulates the attitude and social interaction of grasshopper swarm in terms of gravity and wind advection. In the decision on features extracted by an accelerated feature selection algorithm, popular approaches such as ANN and SVM classifiers had been used. For the evaluation of the proposed model, different data sets of plant leaves were used. The proposed model was successful in the diagnosis of the diseases of leaves the plant with an accuracy of 99.41 percent (average). The proposed biologically inspired model was sufficiently satisfied, and the best or most desirable characteristics were established. Finally, the results of the research for these data sets were estimated by the proposed Fused Modified Grasshopper Optimization Algorithm (FMGOA). The results of that experiment were demonstrated to allow classification models to reduce input features and thus to increase the precision with the presented Modified Grasshopper Optimization Algorithm. Measurement and analysis were performed to prove the model validity through model parameters such as precision, recall, f-measure, and precision.     

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.     

Formal Approach to Workflow Application Fragmentations Over Cloud Deployment Models

Workflow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments. Especially, such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workflow processes and applications with scalable on-demand services. In this paper, we focus on the distribution paradigm and its deployment formalism for such very large-scale workflow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments. We propose a formal approach to vertically as well as horizontally fragment very large-scale workflow processes and their applications and to deploy the workflow process and application fragments over three types of cloud deployment models and architectures. To concretize the formal approach, we firstly devise a series of operational situations fragmenting into cloud workflow process and application components and deploying onto three different types of cloud deployment models and architectures. These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workflow process and applications as an implementing component for cloud workflow management systems. Finally, we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workflow processes and applications to be deployed on cloud deployment models and architectural computing environments as well.     

Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm

Cloud Manufacturing (CMfg) ambitions to create dedicated manufacturing clouds (i.e. virtual enterprises) for complex manufacturing demands through the association of various service providers’ resources and capabilities. In order to insure a dedicated manufacturing cloud to match the level of customer’s requirements, the cloud service selection and composition appear to be a decisive process. This study takes common aspects of cloud services into consideration such as quality of service (QoS) parameters but extend the scope to the physical location of the manufacturing resources. Unlike the classic service composition, manufacturing brings additional constraints. Consequently, we propose a method based on QoS evaluation along with the geo-perspective correlation from one cloud service to another for transportation impact analysis. We also insure the veracity of the manufacturing time evaluation by resource availability overtime. Since the composition is an exhaustive process in terms of computational time consumption, the proposed method is optimised through an adapted Artificial Bee Colony (ABC) algorithm based on initialisation enhancement. Finally, the efficiency and precision of our method are discussed furthermore in the experiments chapter.     

基于资源限制的多目标网络计划优化模型

目前网络计划优化研究要么没有考虑资源限定的柔性,要么只是集中于单纯的工期优化或资源优化等单目标优化。本文针对传统网络计划建模资源限制缺少柔性、优化目标单一等问题进行了深入的研究。在柔性资源的限制下,为使得工程网络计划达到总体最优,考虑工程项目的工期、成本、项目净现值、资源的均衡等多个目标,建立其网络计划优化模型,并采用粒子群算法予以求解。根据拓扑排序算法生成满足时序约束的活动序列并计算活动的时间参数。对于产生资源冲突的活动,依照执行优先权解决冲突资源的执行顺序,更新时间参数。采用随机权重的方法,让粒子群算法种群的多个个体进行随机转化,从而保持解的多样性。采用国际上通用的Patterson问题库中benchmark算例对本文提出的方法进行验证。结果表明,与初始方案相比,优化后的方案分别在工期上缩减了20%,成本上缩减了11.17%,净现值增加了11.82%,资源均衡度减少了18.29%。由此可见,提出的基于粒子群算法的优化模型对资源限制下的网络计划中的工期、成本、净现值、资源均衡度等多个目标均实现了不同程度的优化。     

Joint optimization of preventive maintenance and flexible flowshop sequence-dependent group scheduling considering multiple setups

This study focuses on a joint optimization problem regarding preventive maintenance (PM) and non-permutation group scheduling for a flexible flowshop manufacturing cell in order to minimize makespan. A mixed-integer linear programming model for the investigated problem is developed, which features the consideration of multiple setups, the relaxation of group technology assumptions, and the integration of group scheduling and PM. Based on the model, a lower bounding technique is presented to evaluate the quality of solutions. Furthermore, a genetic algorithm (GA) is proposed to improve computational efficiency. In the GA, a threshold-oriented PM policy, a hybrid crossover and a group swap mutation operator are applied. Numerical experiments are conducted on 45 test problems with various scales. The results show that the proposed model can remarkably reduce makespan. Comparative experiments reveal that the GA outperforms CPLEX, particle swarm optimization and cuckoo search with respect to effectiveness and efficiency.