Elitist Selection Research Articles

A Multi‐Workflow Scheduling Approach With Explicit Evolutionary Multi‐Objective Multi‐Task Optimization Algorithm in Cloud Environment

ABSTRACTWorkflow tasks in the cloud environment are the abstraction and decomposition of large‐scale and complex tasks in real‐world scenarios, so cloud workflow scheduling problems have important research significance. However, most of the existing cloud workflow scheduling schemes are aimed at a single workflow, and do not make reasonable use of the commonality or complementary knowledge between similar tasks. Moreover, most cloud workflow scheduling models mainly focus on a few objectives such as time or cost, which is not comprehensive enough. Therefore, this paper first proposes a multi‐objective cloud workflow scheduling model, which solves the maximum completion time, execution cost and energy consumption as three objectives during task execution. Secondly, to efficiently handle multiple similar cloud workflow scheduling tasks at the same time, this paper treats various cloud workflow scheduling issues as distinct tasks, establishes a multi‐task cloud workflow scheduling framework that aims for the same goal while accommodating workflows of differing scales, and a multi‐objective evolutionary multi‐task optimization algorithm based on elite selection (MOEMT‐ES) is designed to solve the above scheduling model. Finally, through algorithm comparison experiments on the CEC2017 evolutionary multi‐task optimization competition benchmark problem and multi‐workflow test problem, MOEMT‐ES shows superior competitiveness.

Connectivity-Enhanced 3D Deployment Algorithm for Multiple UAVs in Space–Air–Ground Integrated Network

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network.

Research on Multi-Objective Low-Carbon Flexible Job Shop Scheduling Based on Improved NSGA-II

To optimize the production scheduling of a flexible job shop, this paper, based on the NSGA-II algorithm, proposes an adaptive simulated annealing non-dominated sorting genetic algorithm II with enhanced elitism (ASA-NSGA-EE) that establishes a multi-objective flexible job shop scheduling model with the objective functions of minimizing the maximum completion time, processing cost, and carbon emissions generated from processing. The ASA-NSGA-EE algorithm adopts an adaptive crossover and mutation genetic strategy, which dynamically adjusts the crossover and mutation rates based on the evolutionary stage of the population, aiming to reduce the loss of optimal solutions. Additionally, it incorporates the simulated annealing algorithm to optimize the selection strategy by leveraging its cooling characteristics. Furthermore, it improves the elite strategy through incorporating elite selection criteria. Finally, by simulation experiments, the effectiveness of the improved NSGA-II algorithm is validated by comparing it with other algorithms.

Biomechanical Analysis of Smash Stroke in Badminton: A Comparative Study of Elite and Recreational Players: a systematic review

Background and Study Aim: This study aims to conduct a biomechanical analysis of smash shots in badminton with a focus on the comparison between elite and recreational players. In this context, an in-depth understanding of the biomechanical characteristics of smash shots is expected to provide insight into the key factors that differentiate between elite and recreational players. By involving a systematic review of the existing literature, this study sought to make an essential contribution to a comprehensive understanding of the biomechanical aspects of smash shots.Material and Methods: This study utilized a systematic review design by conducting a literature search through several relevant databases. Inclusion and exclusion criteria were established to select studies that fit the research objectives. The selection of elite and recreational players as research subjects was the main focal point, with significant biomechanical variables identified. Data analysis was conducted by detailing the key results related to smash shots of both groups of players. Results: A systematic analysis of the literature revealed significant differences in the biomechanics of smash shots between elite and recreational badminton players. Elite players exhibited more optimized movement patterns, racket speed and body coordination, giving them an edge in smash stroke execution. These findings reinforce the importance of certain biomechanical factors that need to be considered in the practice and development of smash shot skills to achieve higher levels of performance.Conclusions: Based on systematic analysis, the conclusion of this study confirms that elite badminton players have significantly different biomechanical characteristics in smash shots compared to recreational players. The implication is that specialized drills and training can be directed towards improving certain biomechanical aspects to enhance smash stroke performance. This study provides an essential foundation for the development of more effective training methods in improving the technical abilities of badminton players, both for those at the elite and recreational levels. Keywords: Smash Stroke Biomechanics, Badminton, Comparison of Elite and Recreational Players, Systematic Review, Badminton Player Performance Analysis

The Optimization of Urban Traffic Routes Using an Enhanced Genetic Algorithm: A Case Study of Beijing South Railway Station

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system.

Multi-model GWAS reveals key loci for horticultural traits in reconstructed garden strawberry.

The cultivated garden strawberry (Fragaria × ananassa) has a rich history, originating from the hybridization of two wild octoploid strawberry species in the 18th century. Two-step reconstruction of Fragaria × ananassa through controlled crossings between pre-improved selections of its parental species is a promising approach for enriching the breeding germplasm of strawberry for wider adaptability. We created a population of reconstructed strawberry by hybridizing elite selections of F. virginiana and F. chiloensis. A replicated field experiment was conducted to evaluate the population's performance for eleven horticulturally important traits, over multiple years. Population structure analyses based on Fana-50 k SNP array data confirmed pedigree-based grouping of the progenies into four distinct groups. As complex traits are often influenced by environmental variables, and population structure can lead to spurious associations, we tested multiple genome-wide association study (GWAS) models. GWAS uncovered 39 quantitative trait loci (QTL) regions for eight traits distributed across twenty chromosomes, including 11 consistent and 28 putative QTLs. Candidate genes for traits including winter survival, flowering time, runnering vigor, and hermaphrodism were identified within the QTL regions. To our knowledge, this study marks the first comprehensive investigation of adaptive and horticultural traits in a large, multi-familial reconstructed strawberry population using SNP markers.

Application of melt pool profiles for parameter calibration of Goldak’s heat source model

Goldak’s ellipsoidal heat source model is widely accepted in laser powder bed fusion (LPBF) process simulations. The model is dependent on three inherent flux distribution parameters and absorptivity which need to be calibrated before application. However, the uniqueness of the calibrated heat source parameters has not been established, which is crucial to accurately represent the energy density distribution in numerical studies. This paper proposed a novel parametric optimization method that can uniquely calibrate these four heat source parameters with high accuracy. Both the simulated cross-sectional (CS) profile and the mid-front (MF) profile of the melt pool were used during calibration. The differences between the experimental and simulated profiles formed the objective function of the parametric optimization problem, which was calculated by using the least power norm method on the deviations between melt pool boundary curves. A genetic algorithm (GA) with elitism selection approach was employed to optimize the heat source parameters by minimizing this single objective function value. To theoretically verify the applicability of the method, two theoretical case studies involving two distinct sets of heat source parameters were carried out. A unique solution of Goldak’s heat source parameters with less than 2 % error can be achieved within 50 generations of the optimization process. A single-track experiment was conducted to demonstrate the application of the proposed method in a real scenario. The CS and MF melt pool profiles were extracted and the calibration procedure was applied. The profiles from simulation with the calibrated heat source parameters exhibited a good agreement with experimental results. The calibrated parameters were used to simulate multi-track printing on a single layer for validation of the proposed method. The CS melt pool profiles obtained from the multi-track simulation and multi-track experiment agreed well, which validated the accuracy of the proposed calibration method and its utility in obtaining the key Goldak heat source parameters for LPBF simulation.

Enhancing energy efficiency in distributed systems with hybrid energy storage

This paper presents a pioneering approach to enhance energy efficiency within distributed energy systems by integrating hybrid energy storage. Unlike prior research, our study focuses on synergizing two distinct energy storage systems while concurrently optimizing system configuration and operational strategies to enhance performance and reduce costs. The employed distributed energy system incorporates hybrid energy storage, merging thermal energy storage with power storage technologies such as supercapacitors and lithium batteries. We conduct a comprehensive investigation into the impact of this innovative system on distributed energy systems, employing a dual-objective cooperative optimization method that addresses energy efficiency and economic factors alongside environmental considerations. This approach is applied to an almost zero-energy community, demonstrating its efficacy in improving load profiles and optimizing energy management through demand-side management. Additionally, we incorporate stochastic energy management, renewable energy resource planning, and dynamic storage to account for uncertainties in energy sources and demand, utilizing the Two-Point Estimate Method (2P + E PEM). To address the complexity of the problem, we introduce an advanced multi-objective optimization algorithm, the Horse Herd Optimization Algorithm (HOA), inspired by the movement behavior of horses in a herd. This algorithm exhibits superior performance with various operators including elite selection and crossover. In conclusion, our contributions include the introduction of a distributed energy system with hybrid storage, a dual-objective cooperative optimization method, and the application of advanced algorithms. Our results demonstrate significant reductions, with primary energy consumption decreasing by nearly 54.8 % and equivalent pollutant emissions by 63.6 %, highlighting the significance and effectiveness of our proposed approach.

Transformation of the political elite of Kyrgyzstan (1991-2024): Dynamics and analysis

Relevance. This study is relevant as it analyzes the complex transformation of Kyrgyzstan's political elite since independence, examining critical issues like the impact of revolutions, persistence of clan systems, and challenges to democratization. Purpose. The purpose of the study was to analyse the dynamics and transformation of the political elite of Kyrgyzstan since independence to identify the main trends and factors determining its evolution. Methodology. The research uses mixed methods, including legislative analysis, statistical data review, and content analysis of political speeches. Results. The results revealed that the political elite of Kyrgyzstan as a phenomenon still remains a clan-based, elite-patronage system with elements of corruption and requires a rethink of ineffective behavioural practices that have acquired a cyclical character, undermining the effectiveness of a series of revolutions that have occurred. It was highlighted that the increased number of revolutionary events in the country indicates both the strengthening of national identity and the desire to set the country on the path of democratic development, and the difficulties in forming a new political elite due to various factors. A comparative analysis of events in other Central Asian countries demonstrates similar trends in the establishment of political elites and obstacles in the establishment of democracy. Conclusions. The study concludes that the political elite remains largely based on patronage networks despite regime changes. Revolutionary events have not led to sustainable democratic reforms. Gender representation has improved but falls short of goals. Press freedom faces increasing restrictions. The research suggests that achieving stable democratic governance requires addressing clan politics, corruption, and weak institutions. It emphasizes the need for transparent, merit-based systems in political recruitment. The study highlights the ongoing tension between democratic aspirations and authoritarian practices in Kyrgyzstan's political landscape. Keywords: hybridity; cyclicality; solidarity; security; clan elite selection system; elite patronage system

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

In order to achieve the goal of low-carbon, efficient delivery using unmanned vehicles, a multi-objective optimization model considering carbon emissions in the problem of optimizing multi-route delivery for unmanned vehicles is proposed. An improved genetic algorithm (IGA) is designed for solving this problem. This study takes into account constraints such as the maximum service duration for delivery, the number of vehicles, and the approved loading capacity of the vehicles, with the objective of minimizing the startup cost, transportation cost, fuel cost, and environmental cost in terms of the carbon dioxide emissions of unmanned vehicles. A combination encoding method based on the integer of the number of trips, the number of vehicles, and the number of customers is used. The inclusion of a simulated annealing algorithm and an elite selection strategy in the design of the IGA enhances the quality and efficiency of the algorithm. The international dataset Solomon RC 208 is used to verify the effectiveness of the model and the algorithm in small-, medium-, and large-scale cases by comparing them with the genetic algorithm (GA) and simulated annealing algorithm (SA). The research results show that the proposed model is applicable to the problem of optimizing the multi-route delivery of unmanned vehicles while considering carbon emissions. Compared with the GA and SA, the IGA demonstrates faster convergence speed and higher optimization efficiency. Additionally, as the problem’s scale increases, the average total cost deviation rate changes significantly, and better delivery solutions for unmanned vehicles are obtained with the IGA. Furthermore, the selection of delivery routes for unmanned vehicles primarily depends on their startup costs and transportation distance, and the choice of different vehicle types has an impact on delivery duration, total distance, and the average number of trips. The delivery strategy that considers carbon emissions shows a 22.6% difference in its total cost compared to the strategy that does not consider carbon emissions. The model and algorithms proposed in this study provide optimization solutions for achieving low-carbon and efficient delivery using unmanned vehicles, aiming to reduce their environmental impact and costs. They also contribute to the development and application of unmanned vehicle technology in the delivery field.

Crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation

Evolutionary algorithms are popular algorithms for multi-objective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the theoretical foundation of multi-objective evolutionary optimisation (EMO) is still in its early development. Fundamental questions such as the benefits of the crossover operator are still not fully understood. We provide a theoretical analysis of the well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover: we propose classes of “royal road” functions on which these algorithms cover the whole Pareto front in expected polynomial time if crossover is being used. But when disabling crossover, they require exponential time in expectation to cover the Pareto front. The latter even holds for a large class of black-box algorithms using any elitist selection and any unbiased mutation operator. Moreover, even the expected time to create a single Pareto-optimal search point is exponential. We provide two different function classes, one tailored for one-point crossover and another one tailored for uniform crossover, and we show that some immune-inspired hypermutations cannot avoid exponential optimisation times. Our work shows the first example of an exponential performance gap through the use of crossover for the widely used NSGA-II algorithm and contributes to a deeper understanding of its limitations and capabilities.

A modified evolutionary reinforcement learning for multi-agent region protection with fewer defenders

Autonomous region protection is a significant research area in multi-agent systems, aiming to empower defenders in preventing intruders from accessing specific regions. This paper presents a Multi-agent Region Protection Environment (MRPE) featuring fewer defenders, defender damages, and intruder evasion strategies targeting defenders. MRPE poses challenges for traditional protection methods due to its high nonstationarity and limited interception time window. To surmount these hurdles, we modify evolutionary reinforcement learning, giving rise to the corresponding multi-agent region protection method (MRPM). MRPM amalgamates the merits of evolutionary algorithms and deep reinforcement learning, specifically leveraging Differential Evolution (DE) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). DE facilitates diverse sample exploration and overcomes sparse rewards, while MADDPG trains defenders and expedites the DE convergence process. Additionally, an elite selection strategy tailored for multi-agent systems is devised to enhance defender collaboration. The paper also presents ingenious designs for the fitness and reward functions to effectively drive policy optimizations. Finally, extensive numerical simulations are conducted to validate the effectiveness of MRPM.

Bureaucracy and patrimonialism on Wall Street: How organizational forms contribute to elite reproduction

Echoing the recent revival of elite studies, we ask how financialization shapes the composition of contemporary elites and how organizational mechanisms transform its characteristics in terms of class, gender and race. We ask whether the bureaucratization of finance contributed to a ‘purge’ of particularisms. Or to the contrary, whether class, race and gender have become more salient criteria of elite selection with the emergence of neo-patrimonial organizational forms? Using Orbis data on legal forms of financial firms, and original sociodemographic data on founders and managers in key firms, we show that neo-patrimonial organizational forms based on trust networks are spreading within finance. Moreover, we demonstrate the impact of organizational forms on elite reproduction along gender, race and class lines. White men with upper-class background are over-represented in neo-patrimonial firms – mostly found in the hedge fund and private equity industry − compared to bureaucratic firms mostly found in banking. We suggest that financialization is not a modernization process but a recombination of bureaucracy and neo-patrimonial logics.

A particle swarm optimization algorithm based on modified crowding distance for multimodal multi-objective problems

Multimodal multi-objective optimization problems (MMOPs) are commonly encountered in practice. The difficulty in solving MMOPs is obtaining all of the Pareto optimal sets without degrading the performance of the Pareto optimal front. To address this challenge, this study proposes a particle swarm optimization algorithm based on modified crowding distance (MOPSO_MCD). In MOPSO_MCD, a modified method for calculating the crowding distance (MCD) is devised, which allows for a more comprehensive assessment of the crowding relationship between individuals in the decision space and the objective space. Moreover, a cosine similarity-based elite selection mechanism is designed to identify the neighborhood optimal individuals of the individuals in the population and improve the decision space diversity. Additionally, an offspring competition mechanism is proposed to keep the population from trapping in the local optimum and enhance the global search ability of the MOPSO_MCD algorithm. Experimental results and statistical analysis show that MOPSO_MCD performs better than the other comparison algorithms on sixteen test functions and a map-based practical problem.

基于改进遗传算法的苹果收获机器人三维路径规划

In recent years, the problem of “rural labor shortage” in China has become increasingly serious, with a large number of young laborers going out to work, leading to an increasing amount of idle rural land. The intensification of population aging and the reduction of agricultural labor force in China resulted in an urgent demand for agricultural robots. With the rapid development of agricultural machinery and automation technology, agricultural robots have been continuously developing. They can better adapt to the development of biotechnology in agriculture, and traditional harvesting methods may undergo significant changes, with an increased focus on the cultivation of crops. Therefore, this paper introduces a new encoding scheme on the basis of traditional genetic algorithm (GA) and proposes an improved double encoding GA. This new encoding scheme is used on the crossover link, whereas the path node sequence encoding scheme is still used on the mutation link. The selection operation is placed after the mutation, and the merging sorting and elitist selection are performed on the parent population, crossover population, and mutation population before selection, thereby accelerating the convergence speed. On the basis of the improved GA, the three-dimensional path of the apple harvesting robot is designed and planned, with the addition of adaptive adjustment function during the progress. The experimental simulation results show that the three-dimensional path planning of the apple harvesting mobile robot based on the improved GA can minimize the number of paths and loops and well meet the operational requirements of the harvesting robot.

Micropropagation of Duboisia Species via Shoot Tip Meristem

Duboisia is an Australian native, commercially valuable for tropane alkaloid extraction. Clonal propagation of elite selections is essential to establish highly productive plantations. The current propagation system using stem cuttings is proven to be inefficient, prompting the industry to seek a more efficient and effective propagation tool. Tissue culture is a cost-effective alternative for mass propagation of true-to-type plants, particularly ideal for propagating elite Duboisia selections. In this context, attempts were made to develop a commercially viable high throughput micropropagation system for three Duboisia species: Duboisia myoporoides, Duboisia leichhradtii and Duboisia hopwoodii. Various nutrient media, hormone combinations and incubating conditions were tested to optimise each stage of the micropropagation pipeline. The findings revealed that the tissue culture media composition and hormone requirements are species-specific. With the optimised conditions, an efficient tissue culture system was developed, achieving successful meristem induction and multiplication. Species-specific rooting protocol optimisation resulted in 100% rooting for D. myoporoides and D. leichhardtii, and 70% rooting for D. hopwoodii. Furthermore, an optimised acclimatisation protocol supported 100% survival of D. myoporoides and D. leichhardtii and 80% of D. hopwoodii plantlets. This study, for the first time, demonstrated the capacity of successful meristem culture of three Duboisia species, establishing the foundation for high throughput micropropagation of Duboisia species.

An enhanced multimodal multi-objective genetic algorithm with a novel adaptive crossover mechanism for feature selection

Nowadays, multimodal multi-objective optimization problems (MMOPs) have received increasing attention from many researchers. In such problems, there are situations where two or more Pareto Sets (PSs) correspond to the same Pareto Front (PF). It is crucial to obtain as many PSs as possible without compromising the performance of the objective space. Therefore, this paper proposes an enhanced multimodal multi-objective genetic algorithm with a novel adaptive crossover mechanism, named AEDN_NSGAII. In the AEDN_NSGAII, the special crowding distance strategy can provide potential development opportunities for individuals with a larger crowding distance. An adaptive crossover mechanism is established by combining the simulated binary crossover (SBX) operator and the Laplace crossover (LP) operator, which adaptively improves the ability to obtain Pareto optimal solutions. Meanwhile, an elite selection mechanism can efficiently get more excellent individuals as parents to enhance the diversity of the decision space. Then, the proposed algorithm is evaluated on the CEC2019 test suite by the Friedman method and discussed for its feasibility through ablation experiments and boxplot analysis of PSP indicators. Experimental results show that AEDN_NSGAII can effectively search for more PSs without weakening the diversity and convergence of objective space. Finally, the performance of AEDN_NSGAII on the multimodal feature selection problem is compared with that of the other four algorithms. The statistical analysis demonstrates that the proposed algorithm has great potential for resolving this issue.

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

Machine-learning applications to authoritarian selections: The case of China

Elite selection in China has drawn significant attention given the importance of the country. Instead of relying on qualitative assessments from historical and personal insights, this study utilized machine-learning techniques to evaluate the promotion prospects of Chinese elites. By incorporating over 251 individual features of 18,179 officials from 1982 to 2020, I built up an ensemble model to calculate the promotion probabilities of the previous Politburo members of the Communist Party of China (CPC). Methodologically, this study finds that the machine-learning predictions yielded approximately 20% higher accuracy compared to the classical model, which employed the generalized linear model with theoretically identified variables. Moreover, this paper offers valuable insights into Chinese politics by highlighting that Xi Jinping’s selection of central officials has diverged from historical patterns, while his decisions on provincial promotions do not exhibit notable differences from those made by his predecessors.

A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem

In recent years, the flexible job shop dynamic scheduling problem (FJDSP) has received considerable attention; however, FJDSP with transportation resource constraint is seldom investigated. In this study, FJDSP with transportation resource constraint is considered and an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm (DQNSGA) integrated with reinforcement learning (RL) is proposed. In DQNSGA, an initialization method based on heuristic rules and an insertional greedy decoding approach are designed, and a double-Q Learning with an improved ε-greedy strategy is used to adaptively adjust the key parameters of NSGA-III. An improved elite selection strategy is also applied. Through extensive experiments and practical case studies, this algorithm has been compared with three other well-known algorithms. The results demonstrate that DQNSGA exhibits significant effectiveness and superiority in all tests. The research presented in this paper enables effective adjustments of production plans in response to dynamic events, which is of critical importance for production management in the manufacturing industry.