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Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, economy, coordination and operability, and the defects of ant colony optimization algorithm in dealing with mixed pipeline layout conditions are improved. A spatial state transition strategy for optimizing feasible solution search, a pheromone diffusion mechanism for improving pheromone inspiration effect and accelerating algorithm convergence are proposed, and a multi-ant colony co-evolution mechanism is designed for mixed pipeline layout conditions. Based on the secondary development technology, the application of this method in the third-party design software is realized, and verified by a nuclear primary pipeline layout project. The results show that the pheromone Gaussian diffusion multi ant colony optimization (PG-MACO) algorithm has a better performance and layout effect than the traditional ant colony algorithm. The routing efficiency is improved by 58.38%, the convergence algebra is shortened by 43.24%, the pipeline length is shortened by 33.88%, and the number of pipeline bends is reduced by 41.67%, which verifies the effectiveness and engineering practicability of the proposed method.

This paper aims to highlight the latest and most widely used artificial intelligence methods in text similarity prediction. The study focuses on articles published from 2010 to 2022 in the field of text similarity prediction and algorithms applied in this field. In this paper, systematic scientific comparisons have been made between existing approaches for predicting text similarity to answer the question raised in this study about the most used and accurate approach. Through previous studies and the comparisons made in this paper, the Ant Colony Optimization Algorithm (ACO) approach was found to be the most frequently used in text similarity prediction and solving scheduling problems. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aimed at the problems of slow convergence speed and redundant planning paths in the processing of path planning by ant colony algorithm, an improved ant colony algorithm based on obstacle avoidance information and fast optimization search strategy was proposed. In order to improve the first search efficiency and accuracy of the ant colony, the Chebyshev distance was introduced to improve the distance heuristic function, and the guidance of the target point to the robot was enhanced in the transfer probability. The adaptive transfer probability was used to adjust the selection method of nodes during path planning and the setting of initial pheromones based on the distribution of obstacles around the nodes, and the percentage that the ants generate effective paths for the first time increased from 60% to 92%. The garbage information of the generated paths was removed, increasing the pheromone concentration of the optimal path nodes, balancing the local and global searching ability of the ant colony, and speeding up the optimal path. By smoothing the generated paths, the number of robot turns was reduced and the path distance was shortened. The algorithms SSA, ACO, IACO, and I-ACO were selected for performance testing on three grid environments. The results show that the improved ACO algorithm outperforms the other algorithms on path optimization. [ABSTRACT FROM AUTHOR]

Learners confront the issue of navigating an enormous quantity of resources in the developing field of online education, which has been exacerbated by the COVID-19 pandemic. To solve this, our research proposes a novel Learner Path Planning (LPP) model that integrates blockchain and machine learning technologies to maximize the online learning experience. This model employs the ant colony optimization technique, which has been upgraded with blockchain for enhanced security and machine learning for intelligent path planning, to provide a more personalized and efficient learning experience. Our approach determines the extent of concept realization and interaction by examining the interaction degrees of knowledge points, establishing heuristic information and initial pheromone levels for the optimization process. This technique not only optimizes teaching duration based on instructional efficacy, but it also adapts dynamically to individual learner needs. Our empirical data reveal that goal success rates improve significantly across all learner levels. For example, elementary students in 2021 had the highest goal achievement rate of 0.5896. In 2019, intermediate and advanced learners attained rates of 0.7726 and 0.9058, with a significant association between course similarity and target achievement. Blockchain integration ensures secure and transparent processing of educational data, while machine learning algorithms successfully personalize learning routes to meet the various demands of learners. This study not only assists learners in effectively identifying suitable resources, but it also provides useful insights for instructors in improving online teaching approaches. The model's adaptability and scalability make it particularly applicable in the context of the COVID-19 pandemic's rapid developments and problems in the education sector. [ABSTRACT FROM AUTHOR]

Simultaneous use of an efficient routing protocol and mobile sink not only prevents rapid sensor energy depletion but also effectively improves the energy balance in wireless sensor networks. In this paper, a new data aggregation method is proposed called "Ant colony-based Routing and Petal-shaped Data Aggregation (ARPDA)", which includes clustering, Cluster Head (CH) selection, intra-cluster routing, determining polling points, and designing the sink node trajectory. In the proposed method, the network is divided by several virtual concentric circles that are equidistant from each other. The intersection point of these circles with the virtual lines passing through the origin of the network determines the probable polling points for the sink node. First, by efficiently clustering the sensor nodes and forming an intra-cluster routing tree based on the ant colony optimization algorithm, the sensor data is aggregated in CHs; then, by selecting the appropriate polling points and designing a petal-shaped path, the sink node periodically collects the aggregated data in CHs based on two linear and circular movements. Considering the sink movement in the CH selection process is another advantage of the proposed algorithm. Numerical results confirm the better performance of the proposed algorithm compared to the EDT algorithm. [ABSTRACT FROM AUTHOR]

Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance. [ABSTRACT FROM AUTHOR]

This paper studies the time-dependent cold chain logistics vehicle routing problem considering both traffic congestion and carbon emissions. A cold chain logistics model with time-dependent green vehicle paths with time windows (TDGVRPTW) was developed to fulfil the demands of green logistics and to take comprehensive account of consideration should be given to factors such as road congestion and carbon emissions. The objective of the model is to minimise total costs, which include carbon emission costs, penalty costs, fuel consumption costs, fixed costs, damage costs and refrigeration costs. Two-phase hybrid search algorithm was developed to solve this model. During the initial stage of the algorithm, a dual-population ant colony optimization (DACO) algorithm sharing the optimal individual is employed to optimize the distribution route of the vehicle. During the second phase, an adaptive golden section search (AGSS) algorithm is used to optimise the departure time of the vehicle from the distribution centre to avoid traffic congestion time periods. To validate the effectiveness of the suggested two-phase hybrid search algorithm, it is applied to the improved Solomon benchmark test set. The experimental findings demonstrate that the two-phase hybrid search algorithm can reasonably plan the driving routes and departure times for each vehicle, effectively avoiding peak traffic congestion periods in the city, and reducing the overall delivery cost.

With the advancement of education information, the scale of online education has been expanding, which brings challenges to students' learning path planning, i.e., course and learning method planning. To address the limitations of learning path planning such as insufficient personalization, the study proposes a learning path planning method based on learning path variability and ant colony optimization. First, dynamic time regularization is used to obtain learning path variability, and the K-means algorithm is used to classify students' learning types. Subsequently, an ant colony optimization algorithm is used to generate learning paths. Finally, the effectiveness of the method is tested. The results show that the loss value of the ant colony optimization algorithm converges to a minimum value of 0.1, which has the best stability of the loss function curve and the fastest convergence speed compared to other algorithms. Under the same experimental environment, the accuracy of the algorithm is as high as 0.9, which is conducive to the search for the optimal solution. The path planning method designed by the research can effectively grasp the learning characteristics and habits of students, and the accurate classification degree can reach 96.6%. With this learning path planning method, the average video learning time of students reaches a maximum of 80 min, while the average completion rate of students' course objectives is stable at 90%, which is about 20% higher than that of the GA-based learning path planning method. The method can significantly improve academic performance and educational outcomes. The method thus grasps the type of student learning, stimulates students' interest in learning, improves the effect of online learning, helps to promote education informatization and provides a boost to the deep reform of education.

Studies on the allocation of parking demand during the sharing period primarily focus on public parking users, ignoring parking slot providers’ temporary parking demand with heterogeneity. Therefore, this paper takes the allocation of parking slot providers’ temporary parking demand as the research object and establishes a differentiated parking allocation (DPA) model to maximize the platform’s net profit. The model is solved using the ant colony optimization (ACO) algorithm and compared with the First-Come-First-Served (FCFS) algorithm. Then, the platform adopts differentiated or undifferentiated charge measures when charging for the parking slot providers’ temporary parking demand. The numerical analysis is performed to select three indicators for evaluation: 1) the utilization rate, 2) the net profit, and 3) the degree of time fragmentation. Results show that the ACO algorithm has an excellent optimization effect in allocating, and the differentiated allocation-undifferentiated charges for the parking slot providers’ temporary parking demand is feasible. [ABSTRACT FROM AUTHOR]

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection (APFD) metric, comparing it with traditional techniques. It has been applied to a Mobile PaymentWallet application to validate the proposed approach. The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric. The ACO-based technique achieves an APFD of approximately 76%, two percent higher than the second-best optimal ordering technique. These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases, saving time and improving software security overall. [ABSTRACT FROM AUTHOR]

Building a reliable soil model is a prerequisite for accurately calculating the ground potential distribution of the grounding grid. Research shows that the soil electrical parameters are frequency‐dependent, so frequency domain inversion of soil parameters should consider the soil inhomogeneity and the frequency characteristic of parameters. A method for retrieving parameters of horizontal multilayer soil model is proposed. The theoretical formula of apparent complex resistivity with considering dynamic state electromagnetic field theory is derived from Green's function. Discrete dynamic state complex image method is introduced to solve the infinite integral in Green's function. Ant colony optimization algorithm is applied to optimize the inversion process of soil parameters. In the first stage, applicability of this method is confirmed by interpreting field data under DC field. DC parameters including DC resistivity, thickness and number of layer are determined. The performance of different optimization algorithms in terms of computational efficiency is compared at this stage. In the second stage, soil resistivity and relative dielectric constant are retrieved by inverting frequency domain experimental data. Different representations based on quasi‐static electromagnetic field and dynamic state electromagnetic field theories are taken into account. The frequency characteristic of soil parameters is concluded by discussing the inversion results. In the study of parameter inversion of horizontal multi‐layer soil model, our method has shown strong performance in computational efficiency and accuracy of inversion results. [ABSTRACT FROM AUTHOR]

The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.

With the ongoing advancement of AI in intelligent marketing, there is a pressing need for the continuous innovation and evolution of advertising planning courses in higher vocational education to keep pace with environmental shifts. This paper introduces a sophisticated smart learning model, beginning with the construction of a subject knowledge map and detailing its development methodology. Building upon this foundation, the paper enhances the Dijkstra algorithm and integrates it with the ant colony algorithm to offer personalized learning path recommendations. Furthermore, an improved convolutional neural network is employed to generate these customized learning paths. An empirical study is conducted using the advertising planning course of a higher vocational school as a case study. The findings of this research highlight the efficacy of the proposed intelligent learning model in the instructional process. Notably, there is a significant increase in student engagement, with the level of active problem-solving in the classroom rising from an initial average of 14 to 26. Additionally, the average final grades of the experimental group exceeded those of the control group by 7.63 points. The average comprehensive competence score also showed a substantial enhancement, registering at 0.447, indicating a marked improvement in overall student performance.

Driven by artificial intelligence technology and the popularization of the “Internet +” concept, many chemical enterprises have gradually transformed to production automation. However, regarding automation control, these enterprises often still have certain deficiencies. Because of this, this paper proposes an ant colony optimization algorithm to find the best path for the automation control of chemical enterprises. It builds a chemical automation control system based on it. This system mainly focuses on monitoring and managing various aspects of chemical enterprises. After introducing this control system, it conducts in-depth research and Analysis on the production capacity and equipment safety of enterprises. Test results show that the chemical control system constructed in this paper can effectively maintain the standard output of the production equipment in the interval of [16,25] per second, and the maximum output per hour is up to 1017. At the same time, the quality of the production products of the PPM index value continues to be at a high level. In addition, the automated control system can provide early warning of potential equipment hazards by monitoring different levels of current fluctuations. The chemical automation control system proposed in this paper shows excellent performance in detecting and controlling the automated production process in chemical companies.

In this paper, we use information fusion technology to describe the problem formalization of art major talent cultivation and obtain the similarity coefficients and probability matrices of art major talent cultivation by calculating the ability level of art majors. On this basis, the art major talent cultivation path is proposed from two aspects: the planning of the art major cultivation path and the recommendation of a personalized art major academic talent cultivation path. The data set is selected, the experimental environment is determined, and the data analysis software is used to analyze the example of the ant colony optimization algorithm-based talent cultivation path planning for fine arts majors and the CNN-based talent cultivation path recommendation for fine arts majors. The results show that the teaching effect of mastering knowledge points is poor, and the duration has a more obvious change (598s), which confirms that the ant colony optimization algorithm can optimize the teaching duration of video knowledge points according to the situation of talent cultivation path of fine arts majors. CNN, in solving the problem of recommending the talent cultivation path of fine arts majors, compares with the genetic algorithm GA (0.662) and the original ant colony algorithm ACO (0.665); the most personalized learning path recommended is not only more suitable for the user’s learning style, but also in line with the actual learning sequence, and thus can help users efficiently achieve their career goals.

This paper first proposes the direction of constructing a higher mathematics teaching mode supported by intelligent technology and then models the learner portrait of the learning outcome data according to the subject knowledge graph. An improved ant colony optimization algorithm is used to search for the optimal learning path, which is then combined with an improved convolutional neural network to generate a personalized learning path. The Trans R method is used to quantify the relationship between learners and learning resources and a semi-supervised learning conditional random field method based on K-NN is proposed to label learning resources and generate learning accurate evaluation for smart teaching. The smart teaching model of advanced mathematics courses is applied and analyzed in terms of students’ advanced mathematics pre and post-test scores, students’ satisfaction, teachers’ teaching methods and teaching resources in four directions. The analysis obtained that the posttest scores of the learners in the experimental group were 75.631, and the posttest scores of the learners in the control group were 66.314, with a difference of 9.317. The significance level of the variance chi-square test was 0.000

The role of ships is very important for the world economy as a means of transporting goods both between countries and between islands. The selection of ship routes is very crucial in efforts to optimize fuel costs. Application of optimization, Genetic Algorithm and Ant Colony to solve the Asymetric Traveling Salesman Problem (ATSP) model with the minimum fuel cost objective function. This study aims to determine shipping routes for initial/final destinations with lower fuel costs. The results of research on the best route for container ships develop a Traveling Salesman Problem model for decision making for the design of maritime logistics networks with optimum operational costs. The Ant Colony algorithm provides 8 routes with lower fuel costs than the genetic algorithm and the genetic algorithm provides 2 routes with lower costs than the Ant Colony algorithm. This proves that the Ant Colony algorithm is more effective in determining ship routes with the lowest fuel costs.

To efficiently complete a complex computation task, the complex task should be decomposed into sub-computation tasks that run parallel in edge computing. Wireless Sensor Network (WSN) is a typical application of parallel computation. To achieve highly reliable parallel computation for wireless sensor network, the network's lifetime needs to be extended. Therefore, a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network. This paper proposes a task model and a cluster-based WSN model in edge computing. In our model, different tasks require different types of resources and different sensors provide different types of resources, so our model is heterogeneous, which makes the model more practical. Then we propose a task allocation algorithm that combines the Genetic Algorithm (GA) and the Ant Colony Optimization (ACO) algorithm. The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended. The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

The mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of educational technology and the deepening of educational system reform, personalized education has gradually become an important topic in education. However, existing classroom teaching decision-making methods often fail to meet students’ personalized learning needs, resulting in some students being unable to reach their full potential in the classroom. To solve this problem, this study proposed a multi-conditional factor classroom teaching decision optimization method based on the improved particle swarm optimization (IPSO) algorithm, and predicted students’ personalized learning needs by combining with the improved ant colony optimization-support vector regression (IACO-SVR) model. First, the IACO-SVR model was used to collect students’ learning data, such as grades, interests, hobbies and learning progress, to accurately predict their needs in different teaching contexts. Second, the IPSO algorithm was used to optimize the multi-conditional factor classroom teaching decisions, thus meeting the personalized needs of students. The IPSO algorithm had strong global search ability, which effectively found the optimal solution to achieve personalized teaching strategies. It is expected that the teaching quality can be improved by predicting the personalized learning needs of students and optimizing classroom teaching decisions in this study, thus providing better support for their comprehensive development. In addition, the results of this study can provide theoretical basis and reference for administrative departments of education and schools to formulate personalized education policies. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To solve the problems of low efficiency and poor motion stability of DELTA robots in the tomato sorting process of existing tomato processing production lines. Methods: Based on the analysis of the DELTA robot sorting system, the optimized gate shaped trajectory of the cyclotron was used for DELTA robot trajectory planning. By replacing the right angle part of the gate shaped trajectory with a convolutional line, an optimization model with the goal of time optimization was established. The optimized parameters of the gate shaped trajectory were solved using an improved ant colony optimization algorithm. Analyzed its performance through experiments. Results: The proposed method had better average sorting time and sorting success rate compared to the gate shaped trajectory and other methods, with an average sorting time reduction of 11.61% and 1.63%, respectively, and a sorting success rate increase of 2.05% and 0.56%. Conclusion: The proposed planning method effectively improves the sorting accuracy and efficiency of DELTA robots.

With the increasing number of computer network users, the problem of network communication security has become more and more serious. Encryption technology, as one of the key technologies for securing communication, has important research and application values. However, the security of conventional encryption methods is gradually challenged, and at the same time, traditional encryption algorithms require a large amount of computing resources, which makes it difficult to realize real-time encryption and decryption operations. Therefore, how to improve the confidentiality and efficiency of computer network communication security encryption system has become a current urgent problem to be solved. This paper firstly discussed the analysis of its application in the computer network communication security encryption system based on the ant colony optimization (ACO) algorithm. Through the theoretical analysis of the ACO algorithm in terms of bioheuristic optimization algorithm and the analysis of the characteristics of the algorithm, the optimization scheme of key distribution based on the ACO algorithm was discussed. Then, the differences between the ACO algorithm and the traditional encryption algorithm in terms of encryption duration, key distribution time, encryption strength and random number generation speed were further analyzed and verified by experimental data. The data results showed that the key distribution of the encryption system using the ACO algorithm had higher security and better performance compared with the traditional key distribution algorithm. By counting and comparing the data, it was found that the ACO algorithm showed very obvious advantages in terms of encryption and decryption time, key distribution time, encryption strength and random number generation. This indicates that the ACO algorithm has very important application prospects in computer network communication security encryption systems. [ABSTRACT FROM AUTHOR]

In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance.

Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi–objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we address a multiload AGVs workshop scheduling problem with limited buffer capacity. This has important theoretical research value and significance in the manufacturing field in considering the efficient multiload AGVs widely used today, and in the limited buffer area in production practice. To minimize the maximum completion time, an improved ant colony optimization-simulated annealing algorithm based on a multiattribute dispatching rule is proposed. First, we introduce a multiattribute dispatching rule, which combines two attributes, delivery completion time and input queue through dynamic weights that are determined by the information about the system, using the multiattribute dispatching rule to construct the initial solution. Then, with the ant colony optimization-simulated annealing algorithm as the basic framework, we propose a method for calculating transfer probability based on the multiattribute dispatching rule, which obtains heuristic information through the proposed rule. Further, we propose a path branch mechanism and dynamic equilibrium mechanism, aiming to efficiently construct the ant path and dynamically adjust ant path distribution. We propose a key job strategy and design a 2-opt neighborhood search method based on key jobs. Data experiments demonstrate the multiattribute dispatching rule is superior over other heuristic dispatching rules; the algorithm improvement strategies proposed are effective when used simultaneously or separately. Further, the proposed algorithm in this paper is superior over other heuristic algorithms and adapts to all kinds of instances. [ABSTRACT FROM AUTHOR]

Losses in the electrical power transmission and distribution systems are considered two of the most critical challenges in power grids. Reducing the related losses plays a significant role in increasing system efficiency in addition to diminishing costs. Therefore, optimum power transfer as well as finding a convenient route, are essential factors in electrical grids. This paper intends to substantially reduce the transmission/distribution-related losses by finding the shortest and most optimal path between the renewable energy power plant (producer) and the substations/consumers. A genetic algorithm (GA) is proposed for optimal routing to increase the system’s reliability and minimize the losses of the entire network. In this work, by presenting a coding with chromosomes of variable length and considering the construction costs and the power transmission line/path as the fitness function, the appropriate route is obtained. The efficiency of the proposed method is compared with Dijkstra’s algorithm, one of the conventional graph search approaches. The ant colony optimization (ACO) algorithm and a reinforcement learning algorithm, namely the Q-learning model, are employed to further explore the optimization efficiency of the proposed renewable energy-based transmission system. The simulation results demonstrate that the proposed models accurately determine the optimal pathway within an excellent time. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and "reward or punishment" update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the "deadlock" problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved. [ABSTRACT FROM AUTHOR]

This paper develops a new hybrid method based on an improved ant colony optimization algorithm that incorporates pattern search (IACO-PS) for determining the location of faults in a distribution network. The performance of the conventional ant colony optimization (ACO) algorithm is improved using the opposite-based learning strategy to generate the initial population and adding a weight coefficient into the pheromone update mechanism to dynamically adjust the pheromone volatilization factor. The hybrid IACO-PS algorithm combines the individual strengths of ACO and PS. In addition, the fitness function is constructed by counting the false and missing fault information into the fault variable. In optimizing benchmark function experiments, the proposed hybrid IACO-PS presents a superior performance when compared to other improved versions of ACO. The effectiveness of the proposed approach is corroborated by tests performed on an IEEE 134-bus network. Simulation results show that the proposed hybrid IACO-PS method can determine the location of a fault even in the presence of fault distortion. In addition, it is immune to noise and data loss errors. Finally, the method proposed in this paper significantly outperforms other published fault location methods, and it can accurately locate faults and identify the type of distortion. [ABSTRACT FROM AUTHOR]

In order to alleviate the huge amount of calculation when using the continuous domain ant colony optimization (ACO) algorithm to handle the maximum likelihood (ML) estimation problem, we propose a maximum likelihood direction of arrival (ML-DOA) estimation method based on modified ant colony optimization (MACO) algorithm. Firstly, MACO algorithm adopts the elite reverse learning strategy to obtain a better initial solution group. Secondly, the optimization method of ant colony is conducted by combining global cross-neighborhood search and local search of Gaussian kernel function to expand the algorithm search space and accelerate the convergence speed. Finally, the nonlinear global optimal solution of ML estimation method is obtained. Simulation results show that, compared with ML estimation methods based on particle swarm optimization (PSO) algorithm and ACO algorithm, the convergence speed of the ML-MACO algorithm is 4 times faster than that of the ML-ACO algorithm, the computational load is 1/3 that of the ML-ACO algorithm, the resolution success rate is higher than the ML-PSO algorithm and the ML-ACO algorithm, and the estimation error is less than the ML-PSO algorithm and the ML-ACO algorithm. The ML-MACO algorithm maintains the excellent estimation performance of the ML algorithm with lower computational effort, better convergence performance, and higher estimation accuracy. [ABSTRACT FROM AUTHOR]

How an autonomous underwater vehicle (AUV) performs fully automated task allocation and achieves satisfactory mission planning effects during the search for potential threats deployed in an underwater space is the focus of the paper. First, the task assignment problem is defined as a traveling salesman problem (TSP) with specific and distinct starting and ending points. Two competitive and non-commensurable optimization goals, the total sailing distance and the turning angle generated by an AUV to completely traverse threat points in the planned order, are taken into account. The maneuverability limitations of an AUV, namely, minimum radius of a turn and speed, are also introduced as constraints. Then, an improved ant colony optimization (ACO) algorithm based on fuzzy logic and a dynamic pheromone volatilization rule is developed to solve the TSP. With the help of the fuzzy set, the ants that have moved along better paths are screened and the pheromone update is performed only on preferred paths so as to enhance pathfinding guidance in the early stage of the ACO algorithm. By using the dynamic pheromone volatilization rule, more volatile pheromones on preferred paths are produced as the number of iterations of the ACO algorithm increases, thus providing an effective way for the algorithm to escape from a local minimum in the later stage. Finally, comparative simulations are presented to illustrate the effectiveness and advantages of the proposed algorithm and the influence of critical parameters is also analyzed and demonstrated. [ABSTRACT FROM AUTHOR]

Optimal path planning is required for unmanned surface vehicle (USV) operation. This paper proposes two new path planning methods based on ant colony algorithm considering variables such as minimum energy consumption, lowest collision probability with fixed and moving obstacles and the least travelling time. These two methods are called ant colony optimization local search (ACOLS) Out Teta and ACOLS Curve Path. In the ACOLS Out Teta, path is optimized using a new pheromone updating method based on distance with obstacles. In the ACOLS Curve Path method, an innovative way is presented to eliminate curvatures of the path, which reduces the robot's maneuverability, decreasing the path length, energy consumption and travel time. In this paper, collision probability variable is used, providing the ability to present methods for providing an optimal path by paying a reasonable fee. Their performance is compared with each other, and with ACO and PSO. [ABSTRACT FROM AUTHOR]

Motivation: Linear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions. Methods: A novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen–Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate. Result: Three multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale. Availability and implementation: https://github.com/shouhengtuo/MTACO-DMSI/ [ABSTRACT FROM AUTHOR]

Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the risk of ICS through the Bayesian attack graph; next, the target attack path is predicted from multiple angles through the maximum probability attack path and the maximum risk attack path; and finally, the Genetic Ant Colony Optimization Algorithm is used to select the most beneficial protection strategy set for the target attack path under limited budget. Compared with the Genetic Algorithm and Ant Colony Optimization Algorithm, the Genetic Ant Colony Optimization Algorithm proposed in this paper can handle the local optimal problem well. Simulation experiments verify the feasibility and effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]