Genetic Simulated Annealing Algorithm Research Articles

Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

An adaptive heuristic algorithm based on reinforcement learning for ship scheduling optimization problem

Due to the development of ship sizes and the traffic increase in port, ships having long turnaround time in port often result in port congestion, which seriously affects the efficiency of the ship navigation and environmental sustainability of port, it has been evident that effective ship scheduling presents a solution of the fundamental and strategic importance to port congestion. In this paper, a mixed-integer linear programming mathematical model is proposed to realize the optimization of the ship scheduling in port to minimize the total time spent by ships in port. Its methodological novelty is gained by an innovative adaptive genetic simulated annealing algorithm based on a reinforcement learning algorithm (GSAA-RL) to support the developed mathematical model, in which the genetic algorithm is considered as the basic optimization algorithm, and Q-learning with a unique property of selecting suitable parameters dynamically is developed to adjust the parameters of crossover and mutation to improve the search ability of the algorithm. Meanwhile, the dynamic parameter turning process is formulated into a Markov decision process (MDP) model with well defining the state, action, and reward function in GSAA-RL. Specifically, the state sets are proposed by analyzing the key factors affecting the scheduling efficiency and a new reward mechanism that can reduce the objective value significantly based on the quality of selected parameters is designed. The annealing operation is performed on some excellent individuals to further expand the search scope. Simulation experiments demonstrate that the proposed GSAA-RL algorithm can significantly shorten the total time spent by ships in port compared to existing approaches. This study hence helps port operators/planners to improve operational efficiency and reduce port congestion, reduce ship fuel consumption, and deliver goods to cargo owners in a timely manner, which has important practical significance for achieving the “dual carbon” goal.

Optimization of Vehicle Paths considering Carbon Emissions in a Time-Varying Road Network

Regarding the study of the time-dependent green vehicle path problem (TDGVRP), most of the literature uses the step function to represent the vehicle speed change in order to reduce the computation, ignoring the continuity of vehicle speed, which leads to the lack of accurate carbon emissions measurement. This study represents the vehicle speed variation as a continuous function to make the constructed model more consistent with the actual situation, in order to promote the reduction of carbon emissions generated in the logistics and distribution process, improve the greenhouse effect and ecological environment, and ultimately promote sustainable development. In this paper, a simulated annealing-genetic hybrid algorithm (GA-SA) is proposed to solve the constructed optimization model, and two sets of comparison experiments are designed. The experimental results show that compared with the two classical algorithms, the simulated annealing-genetic hybrid algorithm (GA-SA) has better solution performance, inherits the robustness and potential parallelism of the genetic algorithm, and has a higher practical value. Meanwhile, although the total driving distance of the vehicle path considering carbon emissions increases by 3.52 km, the carbon emission cost and the total cost decrease by 5.6% and 3.4%, respectively, which confirms that the path optimization model considering carbon emissions constructed in this study can not only play the role of restraining carbon emissions but also reduce the total distribution cost and the waste of resources. In this study, a continuous function is used to represent the vehicle speed variation, and two classical optimization algorithms (the genetic algorithm and simulated annealing algorithm) are combined and parameter-optimized, and certain innovations are made in the processing of vehicle speed and the solution algorithm. Finally, the effectiveness of the model and algorithm is verified by experiments.

Quantitative provenance analysis through deep learning of rare earth element geochemistry: A case from the Liuling Group of the East Qinling Orogen, Central China

With the ever-growing availability of massive geo-data, deep learning has been widely applied to geoscientific questions such as sedimentary provenance analysis. However, randomly selected initial weights (and also biases) and possible loss of population diversity in traditional neural network learning remain problematic. To address this issue, in this study, we proposed a new deep neural network model by incorporating genetic algorithm (GA) and simulated annealing algorithm into the BP neural network, i.e., the GA-SA-BP model. We then applied this new model to rare earth element (REE) geochemical data of the Liuling Group of the East Qinling Orogen to investigate its provenance. Our results showed that among other deep learning algorithms, the new model presents the best performance with good measuring metrics (e.g., over 85% of accuracy, over 0.82 of F1-macro-average, F1-micro-average, and Kappa coefficient, and smallest (<0.15) Hamming distance). Here, we interpreted in accordance with the classification results that the southern margin of the North China Craton and the South Qinling Orogen are likely two major sources of the Liuling Group, suggesting a bidirectional deposition route of sediments from the north and south. Therefore, we proposed a foreland basin environment as the likely tectonic setting for the Liuling Group, which is consistent with current geological understanding. Our observations suggested that the GA-SA-BP model (or improved deep learning models) coupled with REE geochemistry is capable of provenance analysis.

Study on simultaneous inversion groundwater pollution sources and aquifer parameters based on bilevel programming

Groundwater pollution source identification is the first step of groundwater remediation planning and an important link of groundwater pollution source identification. In order to make the inversion result more accurate, the bilevel programming is proposed to test its availability. In this paper, iterative time, optimization algorithm and noise level are used to test the accuracy of bilevel programming. The advantages and disadvantages of genetic algorithm, multi-objective genetic algorithm and simulated annealing algorithms are compared in searching optimal decision variables. The results show that the combination of multi-objective genetic algorithm and bilevel programming has better accuracy and robustness. The bilevel programming can be used to identify the groundwater pollution source and aquifer parameters.

The covariance intersection fusion estimation algorithm weighted by diagonal matrix based on genetic simulated annealing algorithm and machine learning

SummaryThis paper is concerned with covariance intersection (CI) fusion for multi‐sensor linear time‐varying systems with unknown cross‐covariance. Firstly, a CI fusion weighted by diagonal matrix (DCI) is proposed, and it is proved to be unbiased and robust and has higher accuracy than classical CI fusion. Secondly, the genetic simulated annealing (GSA) algorithm is used for multi‐parameter optimization problem caused by diagonal matrix weights. Considering the serious time‐consuming problem in optimization process of the GSA algorithm, Back Propagation (BP) network is used to obtain the optimal weights. Eventually, the DCI based on GSA algorithm and BP network is proposed. The proposed algorithm has higher accuracy and better stability than classic CI fusion algorithms. Simulation analyses verify the effectiveness and correctness of the conclusion.

A Genetic Simulated Annealing Algorithm for Real-Time Track Reallocation in Busy Complex Railway Station

Track failure at a railway station is a common disruption in the station area caused by abnormal weather or frequent use. This paper focuses on the real-time track reallocation problem to recover the affected track utilization plan and minimize the total train delays and passenger inconveniences. Train platforming operations in busy complex passenger stations are generally conducted according to fixed track utilization rules. In this paper, we presented a mixed-integer linear programming model for train platforming problems with constraints relevant to fixed track utilization rule and objectives of balanced usage of tracks. Furthermore, we proposed an improved genetic simulated annealing algorithm based on improved crossover and selection methods without breaking the fixed track utilization rule constraint. An experiment of Guanzhou East Station with fixed track utilization rules shows the effectiveness of the proposed model and algorithm. The model and algorithm provide efficient approaches for track reallocation problems based on fixed track utilization rules in busy complex passenger stations.

Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network

Nowadays, various machine learning (ML) approaches are widely used in different research areas. However, the need for a large training dataset has restricted the attractiveness of ML techniques for industrial applications, since the preparation of a large dataset is very costly and inefficient. To deal with this limitation, an efficient method is required to fill the gap between industry and research. For this purpose, in this study a transfer learning-based deep neural network (TL-DNN) model was developed to predict the mechanical properties of various graphene reinforced nanocomposites. In this respect, a hybrid multi-layer feedforward DNN was designed, containing one source network and one target network. The source DNN was trained to predict the mechanical properties of graphene/graphene oxide nanocomposites with various matrix types including Al, Cu, PMMA, Si3N4, Al2O3, etc. By transferring the acquired knowledge of the source DNN to the target DNN, the mechanical properties of another material (graphene/epoxy nanocomposite) were estimated with high accuracy level, even with limited number of data samples. It should be mentioned that the optimal values of the network hyperparameters were determined using genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms.

A hybrid optimization algorithm for gate locations in the liquid composite molding process

It is costly to optimize the location of multiple injection gates through a trial and error-based method in the liquid composite molding, even though there are high fidelity physics-based numerical models. A hybrid optimization method called the Simulated Annealing Genetic Algorithm is proposed in this article, which uses the genetic algorithm to provide a global search for a predetermined time and then is further improved by the simulated annealing algorithm. The optimization results of multiple injection gates show that the number of convergence iterations using the Simulated Annealing Genetic Algorithm is less than that using the genetic algorithm, and the phenomenon becomes more obvious as the number of injection gates increases. The case shows that the Simulated Annealing Genetic Algorithm can solve the multiple injection gate configuration problems of highly anisotropic laminates without extra work. The optimization results are in good agreement with the experimental results.

An effective method based on simulated annealing for automatic generation control of power systems

In this study, a novel hybrid Simulated Annealing-Genetic Algorithm (hSA-GA) is proposed. In the hSA-GA, population-based SA is used and each solution in the population is improved using the local search operator. The information exchange between the improved solutions is provided by the crossover operator. A new selection operator is used to ensure the balance between intensification and diversification. The hSA-GA is tested first on nine benchmark functions. Then it is used for tuning proportional–integral–derivative (PID) parameters for automatic generation control (AGC) of multi-area interconnected power systems. Firstly, PID parameters are determined with hSA-GA on a two-area interconnected non-reheating thermal system (System-1) in two different generator time constants. Secondly, to demonstrate the effect of supplementary control in AGC systems, the system is simulated with hSA-GA tuned PID controller and without controller. Additionally, the performance of the proposed hSA-GA is observed on AGC system of two area thermal power system with governor dead-band (GDB) nonlinearity (System-2). Transient responses of Δf1, Δf2 and ΔPtie obtained for both System-1 and System-2 are compared with studies on the same systems in the literature and it is seen that hSA-GA exhibits better control performance on power systems than compared studies. The proposed algorithm shows the best performance in System-1 (when Tg=0.08). Accordingly, settling times of Δf1, Δf2 and ΔPtie are reduced to 2.33 s, 3.783 s and 3.11 s, respectively. Finally, the non-linear two area thermal power system is tested with a load varying between ±50% for 180 s to validation of proposed algorithm and results are compared relevant studies.

Determination of Truck Maintenance Allocation Scheme Based on SA-GA

As an important department of railway transportation and production, large freight train depot is responsible for the regular overhaul and maintenance of railway trucks. The shunting operation of freight train depot covers the whole process of railway trucks entering, storing, overhauling and leaving the depot. It is an important step in the implemen-tation of the maintenance operation. Usually, shunting personnel in the depot transport the trucks to be overhauled to the maintenance line by relying on the shunting operation plan, which is the key to determine the shunting operation plan according to the distribution relationship between vehicles and maintenance. Firstly, this paper analyzes the process of centralized shunting operation in the freight train depot and the factors affecting the time-consuming based on the research idea of flexible workshop scheduling problem. Then, on the premise that the proportion of the weight coefficient will have an impact on the time-consuming of truck busy and shunting in the shunting process, and with the goal of minimizing the time-consuming of truck maintenance busy and shunting, the allocation model between trucks and maintenance lines is established; In addition, an improved genetic algorithm is proposed to solve the established model; Finally, combined with the maintenance of railway trucks in a large freight train depot, an example analysis is carried out on this basis. The results demonstrate that using simulated annealing genetic algorithm to solve the model can obtain the allocation scheme between railway trucks and maintenance operation line. Under the influence of three different coefficients, compared with genetic algorithm, simulated annealing genetic algorithm can reduce the deten-tion time of railway trucks in depot by 0.21%, 0.09% and 0.12% respectively, which is beneficial to reducing the deten-tion time of maintenance vehicles in depot, It plays a positive role in improving the maintenance efficiency of trucks in the depot, and also provides new ideas for the research of railway truck shunting operation.

Multi-UAV Coverage Path Planning for Gas Distribution Map Applications

Aerial coverage for Gas Distribution Map (GDM) using Unmanned Aerial Vehicles (UAVs) with olfaction capabilities has become a key for effectiveness and cost improvements. In this work, the main contribution is the application of Coverage Path Planning (CPP) approaches adapted to survey gas plumes using an autonomous fleet of UAVs. Indeed, the mission is carried out in four steps. First, a gas plume model was integrated to simulate the environment. Then, based on the sensor’s footprint model and the Voronoï diagram, an exact cellular decomposition method is proposed to generate and distribute measurement points or Points Of Interests (POIs) effectively. To take measurements from all these POIs, two planning approaches were proposed in the third step. One is based on the separation of missions between UAVs using the K-means algorithm principle and modeling each path as a Traveling Salesman Problem (TSP). While the second approach models the global mission for all UAVs as a Vehicle Routing Problem (VRP). The resolution of the two mathematical models is achieved using Genetic Algorithms (GAs) and Simulated Annealing (SA) algorithms, respectively. Eventually, the GDM is reconstituted based on triangulation-based cubic interpolation. The evaluation of the proposed simulation techniques based on comparative analyses shows their efficiency, speed and flexibility. The proposed methods take into account the UAV’s flight autonomy and the desired coverage quality, and they provide solutions for a variety of coverage applications, particularly for GDM.

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection.

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%.

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

AbstractCloud computing (CC) is vulnerable for the attacks of current information technology, because it prolongs and uses the information technology infrastructure, traditional operating system, and applications. Several security issues in detecting irregular network performances are confronted by the CC environment. With the intention of solving these security issues, dual‐channel capsule generation adversarial network optimized with red fox optimization algorithm fostered intrusion detection framework (IDS‐CC‐DCCGAN‐RFOA) is proposed in this article, for securing the privacy attacks in CC environment. Initially, the data are taken from NSL‐KDD benchmark dataset. Then the data are given to the preprocessing segment. When using the developed random forest and local least squares in preprocessing segment, the redundancy, and missing values are purged. Then the output of preprocessing is given to the feature selection stage. In feature selection, the optimal features are extracted using univariate ensemble feature selection (UEFS) technique. Based on the optimal features, the data are classified into secured data and privacy attack data based on dual‐channel capsule generative adversarial network. Here, the weight of dual‐channel capsule generative adversarial network is optimally tuned using the red fox optimization algorithm for generating an effective and optimal solution to detect the intruders. The proposed IDS‐CC‐DCCGAN‐RFOA method is implemented in MATLAB. Here, the performance metrics, like accuracy, specificity, sensitivity, F‐score, precision, false positive rate, AUC are analyzed. Then, the proposed IDS‐CC‐CCGAN‐WSOA method provides 13.9367%, 11.12%, 13.268%, and 13.739% higher accuracy and 24.54%, 29.76%, 12.87%, and 34.21% higher sensitivity compared with the existing methods, like intrusion detection framework in cloud computing using deep belief network with salp swarm algorithm (IDS‐CC‐DBN‐CSSA), intrusion detection framework in cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS‐CC‐DNN‐IGASAA), intrusion detection for cloud computing using multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS‐CC‐MLPNN‐ABC), and intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS‐CC‐RCNN‐ALO).

Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.

Fuzzy Demand Vehicle Routing Problem with Soft Time Windows

Considering the vehicle routing problem with fuzzy demand and fuzzy time windows, a vehicle routing optimization method is proposed considering both soft time windows and uncertain customer demand. First, a fuzzy chance-constrained programming model is established based on credibility theory, minimizing the total logistics cost. At the same time, a random simulation algorithm is designed to calculate the penalty cost of delivery failures caused by demand that cannot be satisfied. In order to overcome the shortcomings of GA, which easily falls into the local optimum in the process of searching, and the slow convergence speed of SA when the population is too large, a hybrid simulated annealing–genetic algorithm is adopted to improve the solution quality and efficiency. Finally, the Solomon standard example is used to verify the effectiveness of the algorithm, and the influence of decision-makers’ subjective cost preference is analyzed.

Human-Computer Interaction of Networked Vehicles Based on Big Data and Hybrid Intelligent Algorithm

Intelligent connected vehicle (ICV) refers to realizing the exchange and sharing of intelligent information between vehicles, roads, people, clouds, and so on by carrying advanced on-board sensors, controllers, actuators, and other devices and combining modern communication and network technology. In the age of big data, the information of everything can be transformed into digital resources, and transportation big data has become a basic resource. This paper constructs a big data platform for traffic data processing to realize the function of real-time collection, processing, and analysis of traffic data. Based on the proposed big data platform, the parallel programming framework of MapReduce and HDFS distributed storage system are used to process the real-time vehicle dynamic information in parallel, and the output result is used as the input of running genetic algorithm simulated annealing (GA-SA) for parallel calculation. At the same time, it studies the impact of various elements on users’ interactive behavior, constructs the demand framework and design model of automobile human-computer interaction, and then realizes fast and comprehensive search. The experimental results show that the human-computer interaction method of intelligent networked vehicle can find the optimal driving path, transmit it to each networked vehicle through the human-computer interaction system, realize human-computer interaction, reduce the impact of user unintentional operation on redundant motion, reduce the motion error accumulation of the system, and improve the performance of human-computer interaction system.

Research on improved genetic simulated annealing algorithm for multi-UAV cooperative task allocation

In order to solve the cooperative search problem of multiple unmanned aerial vehicles (multi-UAVs) in a large-scale area, we propose a genetic algorithm (GA) incorporating simulated annealing (SA) for solving the task region allocation problem among multi-UAVs on the premise that the large area is divided into several small areas. Firstly, we describe the problem to be solved, and regard the task areas allocation problem of multi-UAVs as a multiple traveling salesman problem (MTSP). And the objective function is established under the premise that the number of task areas to be searched by each UAV is balanced. Then, we improve the GA, using the advantages of the SA can jump out of the local optimal solution to optimize the new population of offspring generated by GA. Finally, the validity of the algorithm is verified by using the TSPLIB database, and the set MTSP problem is solved. Through a series of comparative experiments, the validity of GAISA and the superiority of solving the MTSP problem can be demonstrated.

Effect Evaluation of Electronic Health PDCA Nursing in Treatment of Childhood Asthma with Artificial Intelligence.

Asthma in children has a long duration and is prone to recurring attacks. Children will feel chest tightness, shortness of breath, cough, and difficulty breathing when they are onset, which has a serious impact on their health. Clinical nursing is of great significance in the treatment of childhood asthma. At present, the electronic health PDCA nursing model is widely used in clinical nursing as a common and effective nursing method. Therefore, it is very important to evaluate the efficacy of the PDCA nursing model in the treatment of childhood asthma. With the development of artificial intelligence, artificial intelligence can be used to evaluate the effect of the PDCA nursing model in the treatment of childhood asthma. The BP network can effectively perform data training and discrimination, but its training efficiency is low, and it is easily affected by initial weights and thresholds. Aiming at this defect, this work uses the genetic simulated annealing (GSA) algorithm to improve it. In view of the problems that the genetic algorithm falls into local minimum and simulated annealing algorithm has a slow convergence speed, the improved genetic simulated annealing algorithm is used to optimize the BP neural network, and an improved genetic simulated annealing BP network (IGSA-BP) is proposed. The algorithm not only reduces the problem that the BP network has an influence on initial weight and threshold on the algorithm but also improves the population diversity and avoids falling into local optimum by improving the crossover and mutation probability formula and improving Metropolis criterion. The proposed method has more efficient performance.