Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.

Investigate the kinetics of coke solution loss reaction with an alkali metal as a catalyst based on the improved genetic algorithm

The kinetics of coke solution loss reaction with and without sodium carbonate were investigated under the reaction atmosphere of carb on dioxide. The variables of gas flow rate and coke particle size were explored to eliminate the external and inteirial diffusion, respectively. Then, the improved method combining with the least square and the genetic algorithm was proposed to solve the homogeneous model and the shrinking core model. It was found that the improved genetic algorithm method has good stability by studying the fitness function at each generation. In the homogeneous model, the activation energy with and without sodium carbonate was 54.89 and 95.56 kJ/mol, respectively. And. the activation energy with and without sodium carbonate in the shrinking core model was 49.83 and 92.18 kJ/mol, respectively. Therefore, it was concluded that the sodium carbonate has the catalytic action. In addition, results showed that the estimated conversions were agreed well with the experimental ones, which indicated that the calculated kinetic parameters were valid and the proposed method was successfully developed.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

An Improved Genetic Algorithm for Allocation Optimization of Distribution Centers

This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorithm (IGA) was proposed to solve the problem. The improvement of IGA is based on the idea of adjusting crossover probability and mutation probability. The IGA is supplied by heuristic rules too. The simulation results show that the IGA is better than the standard GA(SGA) in search efficiency and equality.

Improved genetic algorithm freely searching for dangerous slip surface of slope

Based on the slice method of the non-circular slip surface for the calculation of integral stability of slope, an improved genetic algorithm was proposed, which can freely search for the most dangerous slip surface of slope and the corresponding minimum safety factor without supposing the geometric shape of the most dangerous slip surface. This improved genetic algorithm can simulate the genetic evolution process of organisms and avoid the local minimum value compared with the classical methods. The results of engineering cases show that it is a global optimal algorithm and has many advantages, such as higher efficiency and shorter time than the simple genetic algorithm.

Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new method of detection is proposed based on improved genetic algorithm and extreme learning machine.Taking five different typical diseases with common crops as the objects,this method first preprocesses the images of crops and selects the optimal features for fusion.Then,it builds a model of crop disease identification for extreme learning machine,introduces the hill-climbing algorithm to improve the traditional genetic algorithm,optimizes the initial weights and thresholds of the machine,and acquires the approximately optimal solution.And finally,a data set of crop diseases is used for verification,demonstrating that,compared with several other common machine learning methods,this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.

Improved Genetic Optimization Algorithm with Subdomain Model for Multi-objective Optimal Design of SPMSM

For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.

Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long,this paper analyzes the two-stage resource scheduling mechanism of cloud data center.Aiming at the minimum task completion time,a mathematical model of resource scheduling in cloud data center is established.The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm.On the technology of the conventional genetic algorithm,an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm.The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task,and has good convergence and global optimization ability.

A NEW RETROFIT APPROACH FOR HEAT EXCHANGER NETWORKS—IMPROVED GENETIC ALGORITHM

Inspired by genetic algorithm(GA),an improved genetic algorithm(IGA)is proposed.It inherits the main idea of evolutionary computing,avoids the process of coding and decoding inorder to probe the solution in the state space directly and has distributed computing version.Soit is faster and gives higher precision.Aided by IGA,a new optimization strategy for theflexibility analysis and retrofitting of existing heat exchanger networks is presented.A case studyshows that IGA has the ability of finding the global optimum with higher speed and better preci-sion.

Energy-Efficient Process Planning Using Improved Genetic Algorithm

Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development of process planning methods that take energy consumption into account.An energy-efficient process planning model that incorporates manufacturing time and energy consumption is proposed.For solving the problem,an improved genetic algorithm method is employed to explore the optimal solution.Finally,a case study for process planning is given.The experimental result generates interesting effort,and therefore allows improving the energy efficiency of manufacturing processes in process planning.

MATHEMATICAL ANALYSIS OF MUTATION OPERATOR AND ITS IMPROVED STRATEGY IN GENETIC ALGORITHMS

This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mutation probability in different conditions. The superior convergence property of the new method is evaluated by applying it to two simulation examples.

Method for Fault Feature Selection for a Baler Gearbox Based on an Improved Adaptive Genetic Algorithm

The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox.This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction.The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information.Therefore,this method should be capable of timely diagnosis and monitoring.Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained.The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information,100%fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods.The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.

Time-optimal trajectory planning based on improved adaptive genetic algorithm

This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.

An Improved Genetic Algorithm for UWB Localization

The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.

Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based onGenetic Algorithm

Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions.Unlike conventional turbines,Savonius turbines employ S-shaped blades and have simple internal structures.Therefore,there is a large space for optimizing the blade geometry.In this study,computational fluid dynamics(CFD)numerical simulation and genetic algorithm(GA)were used for the optimal design.The optimization strategies and methods were determined by comparing the results calculated by CFD with the experimental results.The weighted objective function was constructed with the maximum power coefficient Cp and the high-power coefficient range R under multiple working conditions.GA helps to find the optimal individual of the objective function.Compared the optimal scheme with the initial scheme,the overlap ratioβincreased from 0.2 to 0.202,and the clearance ratioεincreased from 0 to 0.179,the blade circumferential angleγincreased from 0°to 27°,the blade shape extended more towards the spindle.The overall power of Savonius turbines was maintained at a high level over 22%,R also increased from 0.73 to 1.02.In comparison with the initial scheme,the energy loss of the optimal scheme at high blade tip speed is greatly reduced,and this reduction is closely related to the optimization of blade geometry.As R becomes larger,Savonius turbines can adapt to the overall working conditions and meet the needs of its work in low flow rate conditions.The results of this paper can be used as a reference for the hydrodynamic optimization of Savonius turbine runners.

Composition of Web Services of Multi-Population Adaptive Genetic Algorithm Based on Cosine Improvement

Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

New Hybrid Genetic Algorithm for Vertex Cover Problems

This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are used to perform global exploration in a population, while neighborhood search methods are used to perform local exploitation around the chromosomes. The experimental results indicate that hybrid genetic algorithms can obtain solutions of excellent quality to the problem instances with different sizes. The pure genetic algorithms are outperformed by the neighborhood search heuristics procedures combined with genetic algorithms.

基于IGA的智能化幕墙施工进度计划编排研究

随着城市高层建筑的快速发展,幕墙施工进度计划编制需要更加高效的智能化方法,因此提出一种基于改进遗传算法的幕墙施工进度计划智能化编排方法。在分析幕墙施工特点后,建立了考虑高空交叉施工作业冲突和施工单元转换约束的最小工期幕墙施工进度计划优化模型,采用改进的遗传算法对该模型进行求解,得到最短工期的施工进度计划。并在一个实际9层办公建筑项目中进行了验证与应用。结果表明,该智能化编排方法可在考虑各种约束条件的同时显著减少施工工期。该方法可为类似幕墙施工项目的进度计划编制提供参考。