Search

This paper presents a low-carbon vehicle routing optimization model to reduce energy consumption and carbon emissions in logistics and distribution. The model is solved using a hybrid algorithm of simulated annealing and ant colony optimization. It enhances the information pheromone concentration update process and directionality by introducing a carbon emission factor and a multifactor operator. Additionally, an adaptive elite individual reproduction strategy is employed to improve algorithm efficiency. In this case study focusing on cold chain logistics distribution, both the model and algorithm under consideration were evaluated. The findings affirm the effectiveness of the model in reducing carbon emissions and demonstrate the efficiency and robustness of the algorithm. Through this analysis, the paper sheds light on environmentally sustainable practices in logistics distribution. [ABSTRACT FROM AUTHOR]

As an important historical and cultural heritage, rural red tourism sites have high historical, cultural, and social values. Moreover, rural red tourism sites are suitable for development and protection as tourism resources due to their unique landscape, architecture, culture, and art. In this paper, we propose a path generation model based on an ant colony algorithm to recommend the best path for tourists to visit rural red tourism sites. First, this paper investigates the modeling methods of path planning and multiobjective planning and their related solution algorithms to prepare for the establishment and solution of the tour path generation model for rural red tourism sites. By analyzing the problem description, this paper proposes the two model objectives of the shortest tour path and the highest total rating of the tourist attraction, and the model limitation of the total tour usage time, to model the model with a multiobjective planning approach. Then, by modifying the calculation of visibility and pheromone increments of the ant colony algorithm, the modified ant colony algorithm can take into account the two objectives of shortest path and highest total rating when constructing the path. Finally, this paper proposes to update the optimal path by using the number of ratings per unit path length as the update criterion of the optimal path. [ABSTRACT FROM AUTHOR]

From the perspective of the new energy vehicle, application of lightweight technology can effectively improve the endurance level of new energy vehicles and promote the steady improvement and rapid development of China's new energy vehicle industry. By keeping in view the importance of lightweight effect of new energy vehicles, in this paper, we have carried out an in-depth analysis of key contents such as new materials, battery weight, carbon fiber technology, and structural design technology in the process of lightweight development of new energy vehicles. Furthermore, this paper introduces the pseudorandom proportional rule to improve the selection method of the seed solution, makes appropriate random disturbance to the center of the pheromone distribution, and reforms the standard deviation of the pheromone distribution. Taking the lightweight of new energy vehicle doors as an example, this paper verifies the new energy vehicle lightweight algorithm based on ant colony algorithm. It is found that the improved continuous domain basic ant colony algorithm is superior to the other two algorithms and it has the capacity to solve optimization problems and has the advantages of high reliability. [ABSTRACT FROM AUTHOR]

Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system. [ABSTRACT FROM AUTHOR]

In order to stabilize the output fluctuation of wind power generation, this paper applies control strategy to control the action of the battery energy storage system according to the stability target, and quickly makes up for the system demand. In this paper, the optimal scheduling of new energy power system with energy storage is studied. This model predictive control is introduced into a certain system and optimized by model predictive control strategy so that the combination can match the predetermined expectations. Then, the model predictive control strategy is used to improve the schedulability of the wind farm. The objective function is to minimize the amount of action, and the composite output can track the scheduling instructions. Considering the cost, the way of storage and operation cost is optimized, and the penalty cost of wind abandonment is also reduced. Through rolling optimization and feedback correction, the model predictive control is optimized and solved, and finally, the action instructions of the storage system are obtained so that the storage output can effectively calm the wind. Power makes the combination schedulable. The optimized wind power/energy storage output is equivalent to the benign and dispatchable power supply of the power grid. [ABSTRACT FROM AUTHOR]

In order to better manage the supplier's inventory and formulate a reasonable distribution plan, based on the existing research, taking the two-level supply chain system composed of one supplier and multiple retailers as the research object and VMI mode as the background, this paper studies the inventory path optimization problem with the goal of minimizing the total cost in the planning period. Taking the retailer's inventory capacity and vehicle capacity constraints as constraints, this method constructs a mixed integer programming model with random demand in multiple cycles and the goal of minimizing the total cost in the planning period. When building the model, the distribution cost is refined. In addition to identifying shipping-related start-up costs and travel costs, processing costs are associated with additional delivery time, which is close to reality. In the algorithm design, the genetic algorithm is combined with the C–W algorithm, a similar hybrid genetic algorithm is used to solve the model, and the sample model is used for estimation; that is, the expected value of the random sample is taken, which is used as the target value for each chromosome. In addition, when optimizing roads, the C–W algorithm is used to divide vehicle capacity according to truck capacity as much as possible, thereby reducing the number of vehicles used and saving the overall total cost of preparation time for certain projects. Facts have proved that the optimized and improved inventory path is more conducive to help enterprises reduce logistics costs and provide theoretical support for enterprise management decisions. [ABSTRACT FROM AUTHOR]

In the era of big data, cloud computing, and machine learning, this has become essential to promote the better development of ideological and political education (IPE) in institutions and universities. In fact, we must pay close devotion to the integration and utilization of online teaching resources, take full benefits of the assistances of big data, machine learning, and continuously collect and sort resources that are conducive to IPE in higher vocational academies, so as to optimize the educational process. In fact, the resource allocation within the context of the IPE is not well-addressed in the existing literature; and the allocation of resources is quite unreasonable. In higher vocational education, the form and content of the IPE will enhance its effectiveness. In this paper, we use the ant colony algorithm to efficiently obtain the solution set for resource allocation, thereby addressing the issues of unreasonable allocation of IPE resources and inefficient testing. In addition, the local search method is incorporated into the ant colony optimization technique to perform a local search on the solution set of the obtained resource allocation in order to increase the algorithm's performance. On the standard test set, algorithm comparison experimentations are carried out to validate the efficacy and efficiency of the suggested algorithm. [ABSTRACT FROM AUTHOR]

With the increasing demand of human resources, the cost of staffing and management is increasing, and it is difficult to dynamically allocate and adjust personnel among different parts. It is the key of intelligent management technology to realize efficient application and mining in human resource management. In the aspect of human resource allocation and management, this paper puts forward the efficient management and application of human resource based on the genetic ant colony algorithm. Firstly, this paper describes the process management and parameter application of the genetic algorithm and ant colony algorithm and manages the resource allocation and management process under the two algorithms. Secondly, several current test functions are applied to the genetic algorithm and ant colony algorithm to test the efficiency of the algorithm, which has obvious advantages in convergence efficiency. Finally, the paper uses the efficient management configuration of human resources for comprehensive application and management and applies the system uploading and downloading services on human resumes, respectively. The genetic ant colony algorithm has obvious advantages in efficiency. In human resource data matching, the genetic algorithm is slightly better than the ant colony algorithm in the case of relatively few data in the early stage, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. The ACO-GA algorithm is more consistent with the actual value, which not only ensures the stability but also ensures the accuracy of prediction, which is more in line with the actual needs. [ABSTRACT FROM AUTHOR]

Reasonable planning of travel routes can keep people away from crowded areas and reduce the probability of contracting the COVID-19. In view of the characteristics related to virus infection and human flow density, it can overcome the shortcomings of using the same pheromone initial value and slow initial convergence in route planning of ant colony optimization (ACO) algorithm. In this paper, the decision tree algorithm is used to divide the human flow density into three levels: high risk, medium risk, and low risk; and different pheromone volatility coefficients are set to change the distribution of pheromone concentration. The experimental results show that the improved ACO algorithm could help to reduce the likehood of passing through the medium-risk areas and the high-risk areas, which is reduced to less than 1%. This scheme provides an efficient route planning method for epidemic prevention and control that can be applied in the daily prevention of COVID-19 in universities. [ABSTRACT FROM AUTHOR]

With the gradual expansion of college scale, the professional categories in colleges and universities are becoming more and more complete, and the volume of courses is becoming more and more huge. In the meantime, the number of students is growing by leaps and bounds, and the teaching resources are subject to more and more complicated teaching tasks. The workload and the difficulty of scheduling in teaching management are also on the rise year by year. This paper proposes a design of a college scheduling algorithm based on an improved genetic ant colony hybrid optimization algorithm. Firstly, the fitness-enhanced elimination law is proposed to improve the selection process of traditional genetic algorithms. Subsequently, the gene infection crossover method is proposed to ensure the increase of the average fitness value in the evolutionary process. Next, the unnecessary replication operation in the traditional genetic algorithm is removed to enhance the operation speed of the algorithm. Finally, the parallel mechanism of fuzzy adaptive is introduced to improve the convergence and stability of the algorithm. For the ant colony optimization algorithm, a nonuniform pheromone distribution is used according to the position of the current raster relative to the starting point, which makes the initial pheromone concentration of the dominant raster higher and avoids blind search by ants. The ant movement rules are redefined by the directional neighborhood expansion strategy to further shorten the path. The experimental results indicate that the hybrid optimization algorithm outperforms other algorithms in terms of performance in terms of scheduling success and scheduling time, and it can be applied in practical scheduling because of the high quality of courses schedule. [ABSTRACT FROM AUTHOR]

In order to optimize the communication research of dynamic carrier sensing control network based on ant colony algorithm, this paper proposes an improved ant colony routing algorithm. Firstly, this paper establishes the experimental model, sets up the relevant experimental parameters, and builds the MATLAB experimental platform. The simulation results show that compared with the basic ant colony algorithm, the improved algorithm performs better in the final jumps. The average residual energy of the sensor node is more. The total energy consumption is less, and the consumption in each round is relatively stable. The residual energy fluctuation range of the sensor node is small, and the energy consumption of each node is relatively balanced. Therefore, the improved ant colony algorithm is important for improving the survival cycle of the WSN. The algorithm comprehensively considers the factors such as convergence speed, node communication distance, node residual energy, and optimal solution and can finally find the path with low node energy consumption and high node residual energy. [ABSTRACT FROM AUTHOR]

With the rapid development of the city, shared bicycle travel has also entered a stage of rapid development. In order to ensure the happiness of citizens traveling, in view of the current supply and demand resources and bicycle scheduling problems, a model of supply and demand resource forecasting and multisite and multivehicle scheduling are established. Supply and demand resource prediction uses characteristic variables based on the graph structure itself as input variables of the prediction model XGBoost (extreme gradient boosting). Combined with the actual situation, the improved ant colony algorithm is used to design a reasonable scheduling for the multisite and multivehicle routing problem (VRP). Using Xiamen City shared bicycle data for simulation training, the results show that the shared bicycle scheduling method proposed in this paper has strong robustness and rationality. [ABSTRACT FROM AUTHOR]

The operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm. [ABSTRACT FROM AUTHOR]

This paper analyzes the path planning problem in the automatic parking process, and studies a path planning method for automatic parking. The grid method and the ant colony optimization are combined to find the shortest path from the parking start point to the end point. The grid method is used to model the parking environment to simulate the actual parking space of automatic parking; then this paper makes some improvements to the basic ant colony optimization, finds the destination by setting the ants' movement rules in the grid, and finds the shortest path after N iterations; since the optimal path found is a polyline, it will increase the difficulty of controlling vehicle path tracking and affect the accuracy of vehicle path tracking. The bezier curve is used to generate a smooth path suitable for vehicle walking. Finally, through matlab simulation, the obstacles in the environment are simulated, and the parking trajectory is obtained. The results show that the path planning method proposed in this paper is feasible. [ABSTRACT FROM AUTHOR]

As a result of the green IoT, application layer users are increasingly seeking more services, and it is particularly important to reduce the energy consumption of an IoT monitoring center while ensuring service reliability. In this paper, the max-min ant colony algorithm is proposed to effectively solve the problems of user service reliability and energy consumption optimization of an IoT monitoring center. First, the service reliability of the application layer users is modeled based on a Markov chain, and the quantitative reliability performance indicators are given. Second, an optimization model between the user service reliability and the energy consumption of an IoT monitoring center is established. Finally, the optimization problem is solved based on the improved max-min ant system. Simulation experimental results show that compared with the improved FFD algorithm, the average power consumption of the max-min ant colony algorithm can be reduced by up to 7.14%. [ABSTRACT FROM AUTHOR]

In the context of the normalization of the epidemic, contactless delivery is becoming one of the most concerned research areas. In the severe epidemic environment, due to the frequent encounter of bayonet temperature measurement, road closure, and other factors, the real-time change frequency of each traffic information is high. In order to improve the efficiency of contactless distribution and enhance user satisfaction, this paper proposes a contactless distribution path optimization algorithm based on improved ant colony algorithm. First of all, the possible traffic factors in the epidemic environment were analyzed, and the cost of each link in the distribution process was modeled. Then, the customer satisfaction is analyzed according to the customer service time window and transformed into a cost model. Finally, the total delivery cost and user satisfaction cost were taken as the optimization objectives, and a new pheromone updating method was adopted and the traditional ant colony algorithm was improved. In the experiment, the effectiveness of the proposed model and algorithm is verified through the simulation optimization and comparative analysis of an example. [ABSTRACT FROM AUTHOR]

Recently, edge-based mobile crowdsensing has become an important sensing technology that takes advantage of mobile devices to collect information about surroundings based on using a group of mobile edge servers that are deployed at the network edge as a link between users and the central server for data filtering and aggregation. Each user may collect multiple data types in mobile collective sensing. For facilitating data aggregation, the same data type carried by various users is assumed to be uploaded to the same mobile edge server. The main problem is determining the server which should be activated to process each data type for reducing the overall cost. In this paper, the problem is formulated as one form of the unqualified multicommodity facility location problem. To solve this problem, two edge-server location strategies are proposed, which use a clustering method for dividing the set of mobile users with data items into clusters and use the ant colony approach to select a mobile edge server for each data type in each cluster. Extensive simulations are conducted based on widely used real data sets. The simulation results show that the proposed strategy achieves better performance than the existing methods in terms of service and facility costs. [ABSTRACT FROM AUTHOR]

In order to optimize robot road planning, automation will increase production efficiency and reduce production costs. An improved ant colony algorithm based on a two-dimensional flat path planning study divided the three-dimensional space into planes, rasterized each plane, and replaced the original shape by storing a pheromone at the intersection. The pheromone storage space along the route will be reduced, and a three-dimensional space route planning study will be gradually conducted. As complex 3D environments and landscape features change, obstacle avoidance strategies have increased, road heuristics have improved, and new heuristic features have emerged. The initial value of the road node pheromone increases the efficiency of early ant search because of the uneven distribution of starting point, target point location information, and forward direction. After each iteration, the stuck ants that did not reach the target point are discarded according to the high-quality ant renewal rules, the iteration threshold is set, and the pheromone fluctuation coefficient is adjusted as the algorithm tends to merge. Compared with the basic ant colony algorithm, the convergence iteration times of the improved ant colony algorithm in this paper are reduced by about 40%, and the optimal path length is shortened by about 10. The duration of the algorithm increases. In terms of algorithm performance, it takes some time to improve the ant colony algorithm. Because of the complexity of the algorithm, some search strategies are added to the algorithm. The contribution of this article is the basis for the mobile robot to walk accurately from the initial position to the working position and perform various tasks independently. [ABSTRACT FROM AUTHOR]

Since the 21st century, artificial intelligence and e-commerce have taken the lead. The two are close to life. Data show that, in the digital economy era, the two have developed rapidly and are widely used; however, they also have some problems. E-commerce logistics system has always been the first problem mentioned by relevant personnel and users. The continuous increase in transaction volume is worth celebrating, but the logistics efficiency continues to be flat or even declining, which has become a serious problem. This shows that, for the e-commerce platform, the investment in science and technology is not comprehensive and in depth, and the artificial intelligence technology has not been deeply applied for the logistics industry. Understanding needs, finding problems, and solving them in time are the way to develop this industry. Pareto is chosen by most logistics systems, but in fact, its efficiency is not as high as imagined. In view of this, this paper proposes an innovative algorithm, based on ant colony algorithm, with optimization mechanism to strengthen the shortcomings, thus improving the premature problem of ant colony algorithm, and at the same time, a series of performances such as convergence speed, accuracy, and stability have been strengthened because of this improvement. Experiments show that the optimization ability of this algorithm is better than the other two. [ABSTRACT FROM AUTHOR]

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique. [ABSTRACT FROM AUTHOR]

With the improvement of people's consumption level and the increasing development of tourism, tourism is becoming more and more popular with everyone, and everyone has more requirements for it. Everyone wants to get the best travel experience for the least money, but because of the size of the world and the number of scenic spots, people usually cannot find some potential routes that interest them. Therefore, our excavation and recommendation of tourist routes can bring convenience to users. The user recommendation system proposed in this paper is to add a word vector, study the similar scenic spots of tourists, generate a data set, and then recommend it to the user for selection. In this way, users can get a route that they are interested in but have never experienced. Through the experimental mode, we also compare the performance of the algorithm in a threshold, similar number of tourists and vector dimension, and get the best values of several indicators, which can make the algorithm reach the best state and ensure the accuracy of users' recommendation. Then, in order to find an affordable and better route for users, we introduce the ant colony algorithm, so we can find the best path. Finally, through the experiment, we can find that the ant colony algorithm has a very good advantage, which can not only save the time for tourists to take public transport but also save the cost of tourism. Through the random survey of 10 users' satisfaction, we can get that this has been a very good promotion. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, urban real estate has gradually become an important commodity for trading, and to some extent, it also affects the trend of the national economy. In view of these limitations and demand, this paper introduces ant group algorithm, by combining the influence of urban real estate changes on the national economic trend; according to the relevant national policy impact, we analyze the national economy changes and fluctuations, try to build a "four-quadrant" model analysis of real estate market impact analysis, and clear national policy on real estate. In order to obtain better economic policies, we promote the healthy and stable development of urban real estate. Simulation experiment proved that ant colony algorithm is effective and supports the change of urban real estate value and national economy. [ABSTRACT FROM AUTHOR]

For the existed biological degenerate primer design problem, a solution method based on an ant colony optimization algorithm is proposed, in which an optimization model considers primer coverage as well as degeneracy while allowing for a small number of base mismatches. Using the construction graph model, the algorithm makes full use of the dynamic change information during the iterative optimal path optimization process, strengthens the explored new path pheromone, and selects the primers that meet the constraints. The results of DNA template sequence matching experiments show that the degenerate primers designed by the proposed algorithm outperform existing approaches. [ABSTRACT FROM AUTHOR]

Dynamic path optimization is an important part of intelligent transportation systems (ITSs). Aiming at the shortcomings of the current dynamic path optimization method, the improved ant colony algorithm was used to optimize the dynamic path. Through the actual investigation and analysis, the influencing factors of the multiobjective planning model were determined. The ant colony algorithm was improved by using the analytic hierarchy process (AHP) to transform path length, travel time, and traffic flow into the comprehensive weight-influencing factor. Meanwhile, directional guidance and dynamic optimization were introduced to the improved ant colony algorithm. In the simulated road network, the length of the optimal path obtained by the improved ant colony algorithm in the simulation road network is 3.015, which is longer than the length of the optimal path obtained by the basic ant colony algorithm (2.902). The travel time of the optimal path obtained by the improved ant colony algorithm (376 s) is significantly shorter than that of the basic ant colony algorithm (416.3 s). The number of iterations of the improved ant colony algorithm (45) is less than that of the basic ant colony algorithm (58). In the instance network, the number of iterations of the improved ant colony algorithm (18) is less than that of the basic ant colony algorithm (26). The travel time of the optimal path obtained by the improved ant colony algorithm (377.1 s) is significantly shorter than that of the basic ant colony algorithm (426 s) and the spatial shortest distance algorithm (424 s). Compared with the basic ant colony algorithm and the spatial shortest distance algorithm, the results of the optimal path obtained by the improved ant colony algorithm were more accurate, and the effectiveness of the improved ant colony algorithm was verified. [ABSTRACT FROM AUTHOR]

Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While the majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved to be successful in producing both accurate and comprehensive classification models in nonstreaming (batch) settings, currently ACObased algorithms for classification problems are not suited to be applied to data stream mining. One of the main challenges is the iterative nature of ACO algorithms, where many procedures--for example, heuristic calculation, selection of continuous attributes, pruning--require multiple passes through the data to create a model. In this paper, we present a new ACO-based algorithm for data stream classification. The proposed algorithm, called Stream Ant-Miner (sAnt-Miner), uses a novel hybrid pheromone model combining both a traditional construction graph and solution archives models to efficiently handle a large number of mixed-type (nominal and continuous) attributes directly without the need for additional procedures, reducing the computational time required to complete an iteration of the algorithm. Our results show that sAnt-Miner produces statistically significant concise models compared with state-of-the-art rule induction data stream algorithms, without negative effects on their predictive accuracy. [ABSTRACT FROM AUTHOR]

Prefabricated components production line optimization is critical for improving industrialized building construction efficiency; however, few studies focus on the production line optimization problem in context of industrialized building construction. In order to optimize the large random orders in the prefabricated components production process, this research proposes a model to minimize variance of the production capacity utilization of prefabricated components in the production cycle, and the ant colony optimization algorithm is introduced to solve the mixed production line sequencing optimization problem. By optimizing the sequence, the production capacity of the component production is balanced, and the capacity utilization rate in the industrialized building construction process is improved. Finally, the effectiveness of the method is verified through a real case of fabricated building components production. The results show that the variance of daily production capacity utilization rate of the optimized hybrid component production line has reduced to 0.53%, which is significantly lower than the 2.45% before optimization. The proposed model could effectively achieve the production capacity balance of prefabricated components production line. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms. [ABSTRACT FROM AUTHOR]

The motion smoothness of 3C locking robot directly affects the machining performance. Improving the motion smoothness can optimize the motion trajectory and reduce the processing time. In this paper, a novel machining path optimization model including motion smoothness is built by employing the coordinate boundary of velocity and acceleration after evaluating the machining motion smoothness of the 3C locking robot. Secondly, based on the creation of the ant colony of adaptive function algorithm, the optimization model of the 3C locking robot in the situation of fixed bolt hole position and floating bolt hole position is resolved. Lastly, the proposed approach collects and analyses a huge amount of data to enable robots to make on-the-fly decisions in the middle of production, even when faced with unexpected circumstances. In the Spark distributed environment, we use the conventional K clustering technique to improve the final output utilizing clustering means. The results show that the machining path optimization of fixed hole considering the motion smoothness improves the smoothness but extends the machining path; the cooperative machining path optimization of multiregion floating bolt holes can significantly improve the motion smoothness and effectively reduce the length of the path. The research results provide theoretical support and design guidance for designers. [ABSTRACT FROM AUTHOR]

This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods. [ABSTRACT FROM AUTHOR]

One ultimate goal of power network planning is to construct power systems that are environmentally friendly. However, it is still a challenging issue to efficiently consider environmental factors in power network planning problems due to the complexity and the diversity of environment. This paper presents a nonlinear approach to formulate the multi-dimensional spatial power network planning problem based on raster map in geographic information systems. With the objective of minimizing investment and operation costs, the proposed model integrates five dimensions: the substation location, the substation sizing, the line type selection, the optimal electric line routing, and the power network evaluation in terms of the ac power flow calculation. Furthermore, the branch capacity constraints and the node voltage limitations at peak load hours are examined to ensure the operational security of power systems. To overcome difficulties in solving the mixed-integer nonlinear optimization problem, an enhanced ant colony optimization with the four-search-space structure and the improved state transition rule is introduced. The case results validate the proposed model and the algorithms. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language. [ABSTRACT FROM AUTHOR]

While the process of intelligent industrial production is accelerating, the application scope of welding robots is also expanding. For the purpose of reducing the work efficiency and time consumption of the welding robot, the ACO is used for the shortest distance and the GA is used for the shortest time fixed-point path trajectory optimization. The application of parameter optimization and random disturbance factor in the ACO increases the global search performance of the algorithm. In the shortest time trajectory optimization, the B-spline curve interpolation method and the GA are combined to carry out the segmental optimization processing. Simulation experiments show that the optimization strategy of ACO can increase the iterative calculation efficiency and path optimization performance of the algorithm. At the same time, the robot with optimized genetic algorithm has smaller fluctuations in joint angle and angular velocity in the simulated welding task, and the optimization algorithm takes 17.6 s less than the traditional particle swarm algorithm and 11 s less than the single A ∗ algorithm. The experiments confirmed the performance of the ACO-GA for the path optimization of the welding robot, and research can provide a scientific path optimization reference for the welding task of the industrial production line. [ABSTRACT FROM AUTHOR]

Internet‐of‐Things (IoT) enabled cyber‐physical systems (CPS) is a system in which communication between the physical devices and the cyber environment runs independently without any user interaction. Several optimization algorithms have been used for determining the optimal solutions that can reduce the production cost and/or enhance the production efficiency with in limited time‐periods. However, existing optimization approaches have failed to solve the issues in the complex manufacturing process. To overcome this issue, a novel technique called directed acyclic graph theory based multiobjective oppositional learnt artificial ant colony resource optimization (DAGT‐MOLAACRO) technique has been introduced in this study for solving the complex manufacturing process in the industry. Initially, IoT devices are used in the industrial sector for sensing and collecting data. Then the collected data is sent to the cyberspace of the CPS system with the least latency. Then, the CPS system collects the data generated from the industrial IoT devices that is stored in cyberspace with lesser memory consumption. MOLAACRO is applied to find the optimal solution among the population that satisfies the resource constraints by constructing the directed acyclic graph. In this way, the DAGT‐MOLAACRO technique reduces the time complexity with minimal latency and computation overhead. For verification purposes, our experimental work has been carried out using different performance metrics such as data latency, time complexity, and computation overhead with respect to the number of IoT devices and the amount of data collected. The results show that the DAGT‐MOLAACRO technique has better performance with reductions in terms of time complexity by 10%, latency by 17%, and the computation overhead by 11% against the existing works in literature. [ABSTRACT FROM AUTHOR]

The quality of economic development is not sufficient, despite the apparent role that public environmental monitoring and management play in the economy's rapid expansion. Therefore, enhancing the standard of economic growth is of the utmost importance. It is vital to establish innovative management techniques to support social and governmental transformation in order to raise the standard of economic development. Economic development affects public administration's fundamental duties and goals. The division of public administration distributes social resources and enhances the market's function. The fundamental problem in public economic management is how to successfully actualize rational resource allocation, advance social justice, and boost social welfare on the basis of resource scarcity and the breakdown of the market mechanism. This study greatly increases the global optimization performance of the fundamental ant colony method and provides the explicit programme and simulation stages for the new algorithm. Finally, simulation tests are conducted using the basic ant colony algorithm and the upgraded ant colony algorithm in relation to the decision-making model of public economic management. According to the simulation findings, the revised algorithm successfully addresses the traditional approach's drawbacks, including its slow convergence speed and propensity to easily enter local minima, and its optimization performance has increased by 30.23%. [ABSTRACT FROM AUTHOR]

Aiming at the stagnation and precocity of the basic ant colony algorithm in solving the film and television transmission path problem, an ant colony optimization algorithm with forgetting factor is proposed. By adding forgetting factor to artificial ants, a new state transition formula is established, and pheromone update strategy is modified. Combined with the optimal error rate of the current solution, the state transition equation is adjusted. Due to the introduction of the forgetting factor, the new formula can be adjusted in time when the original ant colony algorithm calculation falls into the local optimal solution, and the selection and updation of the next node are carried out according to the new state transition scheme. Pheromone, which can be used to reduce the optimal value error, improves the tracking ability of the optimal value, modifies the path evaluation model, and calculates the probability of each path to the current optimal solution. The simulation results of the film transmission path problem (FTPP) example show that the improved algorithm takes less time and the result of path optimization is better. [ABSTRACT FROM AUTHOR]

This article studies the integration and optimization of green innovation capabilities of enterprises. The integration model of enterprise green innovation capability provides the theoretical basis for cultivating enterprise green innovation capability. However, from the perspective of optimization, in the life cycle of the enterprise, how the enterprise should reasonably allocate resources and carry out strategic guidance to achieve the optimal green innovation capability of the enterprise is a question worth thinking about. This stage of research starts from different cycle stages of enterprise development, explores the adaptability and evolution of the integration model, and studies the optimization methods of strategies to achieve the goal of matching. This article adopts the ant colony optimization algorithm to support the research on the integration of green innovation capabilities of enterprises. The article describes the principle of the ant colony optimization algorithm in detail by using the combination of text description and formula. In addition, it analyzes the effect of the research through experiments. The results show that the corresponding CITC coefficients of each item of the Green Innovation Scale are between 0.518 and 0.850, which are all greater than the ideal value of 0.4. [ABSTRACT FROM AUTHOR]

Infrastructure investment has the characteristics of high start-up cost, high initial cost, long cycle, slow recovery, high risk, and high public welfare. Maintenance after completion requires stable financial support, maintenance costs are easily affected by government agencies, and investment efficiency is significantly reduced. This article focuses on the ant colony algorithm audit and supervision to promote the optimization of the new infrastructure investment environment and understands the relevant theories of infrastructure investment on the basis of literature data, and then, the audit supervision based on the ant colony algorithm promotes the optimization of the new infrastructure investment environment. The model is constructed, and the constructed model is tested. The test results show that, first, there is a dynamic equilibrium relationship between total infrastructure investment and GDP, even if it cannot explain the potential relationship between the two. In essence, infrastructure investment has a positive impact on economic growth. As the economic level increases, the demand for infrastructure will increase and infrastructure investment will inevitably increase. Second, the level values of the first order are all nonstationary sequences; after the difference, they are all stable and have the same single integral order. The model meets the preconditions of the cointegration test. [ABSTRACT FROM AUTHOR]

The immature stages of hoverflies of the subfamily Microdontinae (Diptera: Syrphidae) develop in ant nests, as predators of the ant brood. The present paper reviews published and unpublished records of associations of Microdontinae with ants, in order to discuss the following questions. (1) Are all Microdontinae associated with ants? (2) Are Microdontinae associated with all ants? (3) Are particular clades of Microdontinae associated with particular clades of ants? (4) Are Microdontinae associated with other insects?Atotal number of 109 associations between the groups are evaluated, relating to 43 species of Microdontinae belonging to 14 genera, and to at least 69 species of ants belonging to 24 genera and five subfamilies. The taxa of Microdontinae found in association with ants occur scattered throughout their phylogenetic tree. One of the supposedly most basal taxa (Mixogaster) is associated with ants, suggesting that associations with ants evolved early in the history of the subfamily and have remained a predominant feature of their lifestyle. Among ants, associations with Microdontinae are known from subfamilies Ponerinae, Dolichoderinae, Formicinae, Myrmicinae, and Pseudomyrmecinae. These subfamilies comprise more than 95% of all ant species. Interestingly, no associations are known with "dorylomorph" ants (army ants and relatives). [ABSTRACT FROM AUTHOR]

The aim of this paper is to actualize the taxonomy of Dorymyrmex, by addressing problems at both the genus and the species levels. We also explore the taxonomy and distribution of Dorymyrmex in Colombia. We list, diagnose, and key nine species in the country, including three new species: Dorymyrmex amazonicus n. sp. Cuezzo & Guerrero, Dorymyrmex xerophylus n. sp. Cuezzo & Guerrero, and Dorymyrmex tuberosus n. sp. Cuezzo & Guerrero. We provide a detailed description of these new species based on the worker caste and, where possible, other castes. All localities where Dorymyrmex was collected or cited in the literature were mapped to provide a graphical view of its range. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various papers within the issue on the parasites of ants including exploration of ant parasites like virus and bacteria, behavioral and ecological characteristics of parasitism and the impact of infection on ants and its biological control.

With the continuous improvement of living standards, people began to pay more and more attention to sports, and the impact of sports on human health and physique has been paid more and more attention. This study mainly analyzes the scientific impact of sports on human health and physique under the background of big data. Firstly, the big data analytic hierarchy process is used to construct the comprehensive evaluation structure system of sports on human health and physique. Then, an improved big data adaptive ant colony classification rule algorithm is proposed. Finally, the performance evaluation and physical impact analysis of the improved big data algorithm are carried out. The results show that compared with other algorithms, ACA ∗ (ant colony algorithm) based on big data has more obvious advantages in stability, optimization ability, running time, and convergence speed and is more suitable for practical application. In general, the improvement of the physical fitness level of the association members in 2019 mainly depends on the results of the improvement of the physical fitness level. In the future, we need to strengthen physical exercise, change living habits and traffic habits, and other methods to optimize the overall physical fitness. [ABSTRACT FROM AUTHOR]

The cold chain logistics distribution industry not only demands all goods can be timely distribution but also requires to reduce the entire logistics transportation cost as far as possible, and distribution vehicle route optimization is the key problem of cold chain logistics transportation cost calculation. The traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. Specially, other constraints, e.g., the transport time factor, transport cooling factor, and mean road patency factor, can be added to the unified IACO. Meanwhile, the updating mode of traditional pheromone is improved to limit the maximum and minimum pheromone concentration on the road and change the path selection transfer probability. The simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. At the same time, we have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zoological Systematics & Evolutionary Research is the property of Hindawi Limited 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.)

Ant-based clustering methods have received significant attention as robust methods for clustering. Most ant-based algorithms use local density as a metric for determining the ants' propensities to pick up or deposit a data item; however, a number of authors in classical clustering methods have pointed out the advantages of entropy-based metrics for clustering. We introduced an entropy metric into an ant-based clustering algorithm and compared it with other closely related algorithms using local density. The results strongly support the value of entropy metrics, obtaining faster and more accurate results. Entropy governs the pickup and drop behaviors, while movement is guided by the density gradient. Entropy measures also require fewer training parameters than density-based clustering. The remaining parameters are subjected to robustness studies, and a detailed analysis is performed. In the second phase of the study, we further investigated Ramos and Abraham's (In: Proc 2003 IEEE Congr Evol Comput, Hoboken, NJ: IEEE Press; 2003. pp 1370–1375) contention that ant-based methods are particularly suited to incremental clustering. Contrary to expectations, we did not find substantial differences between the efficiencies of incremental and nonincremental approaches to data clustering. © 2009 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Ticks are protected against ants by release of an allomonal defense secretion from the large wax glands (or type 2 glands) that line their bodies. To explore how the large wax glands operate, before and after microscopic observations of these glands (nonsecreted versus secreted test groups), mass determinations were made for Rhipicephalus sanguineus that had been exhausted of secretion by repeated leg pinching to simulate attack by a predator. Prior to secretion, the glandular organ is fully intact histologically and matches the sensillum sagittiforme, a key taxonomic structure described in the 1940s. The large wax gland is innervated and responds to pressure stimulation as a proprioceptor that stimulates the secretory response. Histological observations after secretion has occurred show that the entire glandular contents and associated cells are jettisoned out of the gland like a syringe. The glandular cellular components are subsequently rebuilt by underlying hypodermal cells within a few days so that secretion can take place again. Presumably, the active allomonal ingredients (hydrocarbons) are released when these derived epidermal cells reach and burst onto the cuticular surface. Our conclusion is that the large wax glands are holocrine and feature intermittent regeneration. [ABSTRACT FROM AUTHOR]

We compare the performances of established means of character selection for discriminant analysis in species distinction with a combination procedure for finding the optimal character combination (minimum classification error, minimum number of required characters), using morphometric data sets from the ant genera Cardiocondyla, Lasius and Tetramorium. The established methods are empirical character selection as well as forward selection, backward elimination and stepwise selection of discriminant analysis. The combination procedure is clearly superior to the established methods of character selection, and is widely applicable. [ABSTRACT FROM AUTHOR]