Elite Retention Strategy Research Articles

Integrated carbon footprint with cutting parameters for production scheduling

A large part of the global carbon footprint comes from manufacturing. Apart from material distribution during product assembly, rational optimization of cutting parameters and production scheduling during processing are the main ways to realize low-carbon manufacturing. The existing researches mainly focus on cutting parameters or production scheduling when reducing manufacturing carbon emissions, but seldom considers the coupling relationship between the two. This limits the reduction of emissions to some extent. On this basis, a collaborative low-carbon optimization system suitable for the actual use of enterprises was developed. The optimization of production scheduling is a discrete problem, while cutting parameters are continuous and processing time brought by different cutting parameters will cause different effects on the scheduling scheme. Therefore, a two-stage optimization strategy is adopted to achieve collaborative optimization of cutting parameters and production scheduling. To achieve multi-objective optimization of the model and speed up convergence, the Crowding Niche mechanism and elite retention strategy are introduced into the selection operator to improve the genetic algorithm based on Pareto Optimality. In addition, different from the traditional that need to manually determine the machine tool and cutting tool according to the demand, a knowledge base module is integrated into the system. The knowledge base module is qualified for the knowledge acquisition and instance matching of the model in order to meet the processing capacity and constraints and enhance the ability of intelligent decision-making during production. Finally, the interactive application interface of the collaborative low-carbon optimization system has been implemented and the effectiveness of the system is verified by taking the manufacturing case of electromechanical enterprises as an example.

Digital-twin-based job shop multi-objective scheduling model and strategy

ABSTRACT Job scheduling is an important part of production management and an effective method for increasing productivity, improving enterprise competitiveness, and reducing production energy consumption. However, in traditional scheduling, some problems, such as low information transparency, response lag, poor accuracy, and poor optimisation, affect the scheduling performance. Therefore, this study presents a digital-twin-based (DT-based) job shop scheduling strategy to address these problems. First, energy consumption is introduced, and a job shop scheduling model that minimises the completion time, tardiness, and energy consumption is established. Then, a scheduling decision framework based on cloud-edge computing is proposed, and the DT-based job shop system composition and operating mechanism are described. In addition, a DT-based shop scheduling strategy, comprising an overall scheduling mechanism, an accurate processing time determination method that uses a time and space compression ratio simulation, and a data comparison and abnormality determination method, is designed. Moreover, the non-dominated sorting genetic algorithm (NSGA-II) is improved by incorporating a multi-mode crossover and random variation method, along with a variable proportion elite retention strategy. Finally, the effectiveness of the improved algorithm and proposed strategy is verified using a standard dataset and practical processing problem.

Edge-side task scheduling: Auction mechanism and genetic algorithm based methods

Edge computing is an emerging computing architecture. The scheduling and optimization of the tasks on the edge side of the smart factory can effectively reduce the processing delay and improve the utilization efficiency of servers. This study focuses on the problem of edge-side task scheduling with the goal of minimizing the maximum completion time of the tasks. A first-price sealed-bid auction based algorithm and a genetic algorithm with elite retention strategy are designed to solve the problem. The experimental results indicate that the auction-based scheduling algorithm has better real-time performances compared to the genetic algorithm.

Cooperative Area Search Algorithm for Multi-UAVs in Uncertain Battlefield Environment

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperative area search in uncertain battlefield environment, a multi-UAVs cooperative area search algorithm based on co-evolutionary genetic algorithm (CEGA) is proposed. Firstly, the raster Map and extended Search Probability Map (SPM) model of the task area are constructed, the UAV state model and sensor detection model are established, and the search environment updating method based on the Bayesian criterion is presented. Then, considering the cooperative elements of multi-UAVs, the cooperative area search planning model is established, and based on the rolling optimization idea, the co-evolutionary genetic algorithm with elite retention strategy is used to optimize the solution, and the multi-UAVs cooperative search paths are generated. Through simulation experiments and comparative analysis, the effectiveness of the proposed algorithm is verified.

Optimising the Distribution of Multi-Cycle Emergency Supplies after a Disaster

In order to achieve rapid and fair distribution of emergency supplies after a large-scale sudden disaster, this paper constructs a comprehensive time perception satisfaction function and a comprehensive material loss pain function to portray the perceived satisfaction of disaster victims based on objective constraints such as limited transport, multimodal transport and supply being less than demand, and at the same time considers the subjective perception of time and material quantity of disaster victims under limited rational conditions, and constructs a multi-objective optimisation model for the dispatch of multi-cycle emergency supplies by combining comprehensive rescue cost information. For the characteristics of the proposed model, based on the NSGA-II algorithm, generalized reverse learning strategy, coding repair strategy, improved adaptive crossover, variation strategy, and elite retention strategy are introduced. Based on this, we use the real data of the 2008 Wenchuan earthquake combined with simulated data to design corresponding cases for validation and comparison with the basic NSGA-II algorithm, SPEA-II and MOPSO algorithms. The results show that the proposed model and algorithm can effectively solve the large-scale post-disaster emergency resource allocation problem, and the improved NSGA- II algorithm has better performance.

An improved genetic algorithm with dynamic neighborhood search for job shop scheduling problem.

The job shop scheduling problem (JSP) has consistently garnered significant attention. This paper introduces an improved genetic algorithm (IGA) with dynamic neighborhood search to tackle job shop scheduling problems with the objective of minimization the makespan. An inserted operation based on idle time is introduced during the decoding phase. An improved POX crossover operator is presented. A novel mutation operation is designed for searching neighborhood solutions. A new genetic recombination strategy based on a dynamic gene bank is provided. The elite retention strategy is presented. Several benchmarks are used to evaluate the algorithm's performance, and the computational results demonstrate that IGA delivers promising and competitive outcomes for the considered JSP.

Robust optimization of agriculture products urban distribution path considering demand uncertainty

Given the optimization problem of agriculture products logistics distribution path in the city, the demand of retail terminals is uncertain because consumer demand is affected by environmental and social factors, and the agriculture products urban distribution optimization model based on the determined demand cannot effectively solve the distribution problem with uncertain demand. Based on the demand determination model, this paper constructs a robust optimization research model based on scenario sets and reallocates distribution paths according to the change of demand. A robust optimization model of the agriculture products urban distribution path under multiple scenarios is built on the basis of meeting the mixing time window and using the objective function of total cost agriculture products urban distribution optimization model. To solve the problem, an improved genetic algorithm was designed by introducing elite retention strategy and migration strategy, which improved the defect of the traditional genetic algorithm which was easy to precocious convergence. Compared with the genetic algorithm, the proposed algorithm achieved the minimum total cost in all scenarios, the average total cost was reduced, the average convergence time of the algorithm was reduced by 5%, and the average total vehicle running distance was reduced by 3%.

Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.

A two-variable control and optimization method for imbalance of high pressure compressor based on improved genetic algorithm.

To solve the problem of low quality rate for one-time assembly of high-pressure compressors, an improved genetic algorithm (GA) is used to adjust and optimize the imbalance after assembly. This paper takes the post-assembly imbalance of a multi-stage rotor of a high-pressure compressor as the objective function, to reduce the post-assembly imbalance by adjusting the arrangement order of rotor blades and the assembly phase between rotors. We used a four-sector staggered distribution method to generate high-quality initial populations and added an elite retention strategy. The crossover and mutation probabilities are adaptively adjusted according to the fitness function values. The threshold termination condition is added to make the algorithm converge quickly so as to achieve fast, stable, and efficient search. The simulation results show that the imbalance is reduced by 99.46% by using the improved genetic algorithm, which is better than the traditional GA. The experimental results show that the imbalance of the two correction surfaces can be reduced to 640 and 760 g·mm, respectively, which is 86.7% and 87.1% better than the zero-degree assembly.

Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging

Because of their flexibility, controllability and convenience, Automated Guided Vehicles (AGV) have gradually gained popularity in intelligent manufacturing because to their adaptability, controllability, and simplicity. We examine the relationship between AGV scheduling tasks, charging thresholds, and power consumption, in order to address the issue of how AGV charging affects the scheduling of flexible manufacturing units with multiple AGVs. Aiming to promote AGVs load balance and reduce AGV charging times while meeting customer demands, we establish a scheduling model with the objective of minimizing the maximum completion time based on process sequence limitations, processing time restrictions, and workpiece transportation constraints. In accordance with the model’s characteristics, we code the machine, workpiece, and AGV independently, solve the model using a genetic algorithm, adjust the crossover mutation operator, and incorporate an elite retention strategy to the population initialization process to improve genetic diversity. Calculation examples are used to examine the marginal utility of the number of AGVs and electricity and validate the efficiency and viability of the scheduling model. The results show that the AVGs are effectively scheduled to complete transportation tasks and reduce the charging wait time. The multi-AGV flexible manufacturing cell scheduling can also help decision makers to seek AGVs load balance by simulation, reduce the charging times, and decrease the final completion time of manufacturing unit. In addition, AGV utilization can be maximized when the fleet size of AGV is 20%-40% of the number of workpieces.

Maximum available capacity evaluation of distribution network considering photovoltaics and electric vehicles

The surge of new loads such as the electric vehicles (EVs) and the large-scale integration of photovoltaics (PVs) pose a significant impact on the safe and economic operation of the distribution network. To evaluate the maximum available capacity of the distribution network, this paper analyzes the charging profile of the EVs, and established the model that predicts the medium- and long-term charging load of the EVs. Then, the maximum available capacity evaluation model that considers the distribution network reconfiguration under the influences of the EVs and PVs is developed. The proposed model is solved using the second-order cone relaxation technique. Besides, to determine the best location of the PVs, the genetic algorithm with the elite retention strategy is adopted to obtain the optimum allocation of the PVs. Simulation results carried out on the modified test system that includes two IEEE 33-bus systems show that the proposed method can accurately evaluate the maximum available capacity of the distribution network, and quantify the effect of the network reconfiguration and PVs on the maximum supply capability of the distribution network. In addition, the proposed method can also provide the theoretical support for the optimum investment in the distribution network.

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm.

Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.

Multi-objective flexible job-shop scheduling problem researching based on improved adaptive NSGA-II

To solve the scheduling problem of products manufacturing in the military electronic equipment enterprises, a multi-objective flexible job-shop scheduling model was established with the objective of minimum the make span, production costs and equipment load. According to the characteristics of flexible job-shop scheduling problem, an adaptive NSGA-II algorithm was proposed in this paper, which could change the probabilities of crossover and mutation operation at different stages of genetic processes. To improve the diversity of population, the algorithm performed independent crossover and mutation operations on the working processes and equipment. Besides, a population independent elite retention strategy was adopted to avoid the loss of optimum solutions. At last, the feasibility and validity of the improved adaptive NSGA-II are verified by a flexible job-shop example.

A Production Scheduling Method for Resolving Multiple Equipment Operation Conflicts in Lithium Battery Mills

Due to the wide variety of production orders and complex processes in the lithium battery cell rolling shop, it is easy to cause conflict deadlock problems when multiple devices are operating at the same time, thus blocking the tasks and affecting productivity. In this paper, we build a task scheduling framework for the rolling shop by combining the production process of lithium battery cells, and establish a production scheduling model for the rolling shop by considering the scheduling rules of order splitting and order prioritization. An improved genetic algorithm is proposed to solve the model with the completion time and delay time as the optimization objectives. First, heuristic rules are combined with GLR initialization methods to generate initial populations to improve population quality. Secondly, a simulated annealing algorithm with an elite retention strategy is introduced to screen the new population, and a neighborhood exchange strategy based on order priority ranking is proposed to overcome the problem that the iterative process is prone to illegal solutions. Finally, the encoding method and mutation repair mechanism are improved, and adaptive crossover and mutation operations are used to improve the search capability of the algorithm. The experimental results show that the improved genetic algorithm is feasible and effective in solving the optimization problem of rolling operation shop scheduling, and the algorithm has certain application value in improving the efficiency of smart shop operation.

Adaptive Grouping Weapon-Target Assignment with Field-of-view Angle Constraint

The problem of weapon-target assignment for intercepting multiple targets is considered with adaptive grouping and field-of-view angle constraints. In view of the factors influencing the assignment efficiency, the interception probability is evaluated by the criterion indexes of intercepting angle, relative distance, relative speed and required acceleration. A weapon-target assignment method is proposed to achieve effective interception for all targets and avoid over-allocation of missiles. The genetic algorithm with elite retention strategy is used to solve the optimal allocation problem in cosideration of adaptive grouping and field-of-view angle constraints. The simulation results for multi-to-multi assignment verify the proposed strategy with the satisfactory performance.

An Improved Particle Swarm Algorithm with the Elite Retain Strategy for Solving Flexible Jobshop Scheduling Problem

As a combination optimization problem that an enterprise often encounters in the process of jobshop production management, the flexible job shop scheduling problem has become one of the hot issues that scholars at home and abroad have continuously studied and explored in recent years. With the development of computer technology and the rise of interdisciplinary, intelligent optimization algorithms have gradually become the primary method to solve such problems. Particle swarm algorithm is an evolutionary search calculation method that simulates birds gathering flight, constantly changing their position and speed during movement, and finally reaching the optimal state. However, the application of this algorithm in the field of solving jobshop scheduling is not particularly mature, and there are some shortcomings in its application. Aiming at the disadvantages of particle swarm algorithm that it is easy to fall into the optimal local solution and the slow convergence speed of the algorithm in the later stage, this paper proposes an elite retention strategy and integrates it into the PSO to enhance its later solution ability.

Multi-objective Green Flexible Workshop Scheduling Considering Multi-energy Consumption Factors

In order to solve the energy consumption problem of shop floor caused by machine processing, adjustment, idling and workpiece transportation, the green flexible workshop scheduling problem was studied, and a multi-objective optimization model was established with the maximum completion time, total machine load and total energy consumption as the objectives. The double-layer coding strategy was adopted to encode processes and machines respectively to optimize the scheduling scheme, and then the adaptive hybrid crossover scheme, mutation scheme and improved elite retention strategy were adopted to improve the operation efficiency and population diversity of NSGA-II algorithm. Finally, a case study is used to verify the effectiveness of the algorithm in solving the multi-objective green flexible workshop scheduling problem, which provides a reference for enterprises to achieve green manufacturing.

Application of improved fireworks algorithm in grinding surface roughness online monitoring

The enclosed machining environment, the cutting fluid impact on the workpiece, the danger posed by high-speed rotation of the grinding wheel and the highly dynamic nature of the machine tool makes online monitoring surface roughness a self-evident conundrum in the grinding process. Guided by this difficulty, an online monitoring method of the grinding surface roughness based on Improved Fireworks Algorithm (IFWA) was proposed. According to the frequency spectrum analysis of grinding force signal and surface texture curve, and with the help of chi-square distribution density function, the functional relationship between grinding force signal and surface texture amplitude-frequency curve was established. Then, the Inverse Discrete Fourier Transform (IDFT) of the aforementioned mathematical function was executed to procure the mathematical relationship of the grinding force signal expressing the surface texture curve. Furthermore, the least square method was used to calibrate the profile least-square median of the surface texture curve. The calculation process of the surface texture curve to the surface roughness was then incorporated into the IFWA. Eventually, the elite retention strategy was introduced into the standard Fireworks Algorithm (FWA). An IFWA was designed to optimize the attenuation coefficient in the grinding force signal and surface texture curve function model. Taking the plane grinding of silica ceramic composites as an example, a theoretical model for online monitoring the grinding surface roughness was established based on the experimental data, and the prediction results in the accuracy of the method was the highest by comparing various algorithms. According to this model, the online monitoring of grinding roughness can be accomplished by the real-time acquisition of force signals in the grinding process.

Optimal design of transverse field motor based on hybrid algorithm of harmony search and differential evolution

The traditional optimal design of a transverse magnetic field motor mainly uses the electromagnetic relational formulas and the finite element analysis to empirically select its structural parameters, therefore making it difficult to obtain the global optimal solution of each parameter and meeting the requirements for multi-objective optimization. This paper suggests a hybrid algorithm of harmony search and differential evolution and introduces the elite retention strategy to improve the hybrid algorithm. It presents the principles of the hybrid algorithm, and then the algorithm performance test is used to verify the superiority of the hybrid algorithm. The electromagnetic analysis demonstrates the influence of the main dimensional parameters of the transverse magnetic field motor on its maximum torque density, therefore providing the basis for its optimal design. Jointly with the hybrid algorithm, the maximum torque density and the production cost are used as the objective function to optimize the transverse magnetic field motor under two groups of weighted coefficients. The simulation results show that the maximum torque density of the transverse magnetic field motor optimized with the hybrid algorithm is larger compared with the prototype motor but its production cost is lower.

Real-Time Estimation of GPS-BDS Inter-System Biases: An Improved Particle Swarm Optimization Algorithm

The restart of the receiver will lead to the change in the non-overlapping frequency inter-system biases (ISB), which will make it difficult to apply the tightly combined RTK method of pre-calibrating ISB to the actual scene. Particle swarm optimization (PSO) algorithm can be used to estimate the fractional part of the inter-system phase bias (F-ISPB) in real time, which is not affected by the receiver restart. However, the standard PSO can easily fall into local optimum and cannot accurately estimate the value of F-ISPB. In this contribution, based on the characteristics of F-ISPB, we propose an improved PSO with adaptive search space and elite reservation strategy to estimate the F-ISPB in real time. When the value of F-ISPB is close to the boundary of the search space, the improved PSO will transform the search space so that F-ISPB will be located near the central region of the new search space, which will greatly reduce the situation of the standard PSO easily falling into local optimum. Since F-ISPB is very stable, an elite retention strategy will help us to estimate F-ISPB faster and more accurately. Three sets of short baseline static data were selected for testing. The results show that the inter-system differenced model based on the improved PSO has a higher ambiguity fixed rate and positioning accuracy than the inter-system differenced model based on the standard PSO and the classical intra-system differenced model, and the fewer the number of satellites, the more obvious the effect.