Ant Algorithm Research Articles

Two-stage algorithm for automatic repair of pavement cracks

PurposeTo develop an automated system for identifying and repairing cracks in asphalt pavements, addressing the urgent need for efficient pavement maintenance solutions amidst increasing workloads and decreasing budgets.Design/methodology/approachThe research was conducted in two main stages: Crack identification: Utilizing the U-Net deep learning model for pixel-level segmentation to identify pavement cracks, followed by morphological operations such as thinning and spur removal to refine the crack trajectories. Automated crack repair path planning: Developing an enhanced hybrid ant colony greedy algorithm (EAC-GA), which integrates the ant colony (AC) algorithm, greedy algorithm (GA) and three local enhancement strategies – PointsExchange, Cracks2OPT and Nearby Cracks 2OPT – to plan the most efficient repair paths with minimal redundant distance.FindingsThe EAC-GA demonstrated significant advantages in solution quality compared to the GA, the traditional AC and the AC-GA. Experimental validation on repair areas with varying numbers of cracks (16, 26 and 36) confirmed the effectiveness and scalability of the proposed method.Originality/valueThe originality of this research lies in the application of advanced deep learning and optimization algorithms to the specific problem of pavement crack repair. The value is twofold: Technological innovation in the field of pavement maintenance, offering a more efficient and automated approach to a common and costly issue. The potential for significant economic and operational benefits, particularly in the context of reduced maintenance budgets and increasing maintenance demands.

Optimization of Neighborhood Public Space Design Based on Physical Environment Simulation and Crowd Simulation—A Case Study of Xiaomi’s Changping Campus

Digital technology has brought drastic changes to the design methods, values, and design tools of neighbourhood public spaces, thereby changing the behavioural patterns of people in neighbourhood public spaces. Therefore, people’s requirements for urban public space have changed and are characterised by high efficiency, high precision, humanization, and high aesthetics. Scholars at home and abroad have conducted many studies and practises on the application of digital technology to neighbourhood design, but there is a lack of systematic research practises on the use and analysis of multi-faceted data. This paper selects the Xiaomi Beijing Changping II design project, simulates and deduces the physical environment of its location, simulates the results of the natural environment by using the data of sunshine, wind, and water catchment in the physical environment, and then simulates the crowd’s action paths by combining kinetic algorithms and ant algorithms to optimise the design methods, processes, and results of the neighbourhood public space on the basis of this research. The research team designed five groups of programmes for the project based on different design methods and processes, and conducted a comparative study of the five groups of programmes through the hierarchical analysis method in conjunction with the fuzzy comprehensive evaluation method, as well as discussing them in conjunction with the actual bidding results of the project and the scoring conclusions of the industry experts. The results of the study show that the design scheme for neighbourhood public space based on physical environment simulation and crowd simulation is better able to take advantage of the analytical and predictive advantages of the technology and unite with the designer’s aesthetic interests, balancing the data objectivity and aesthetic subjectivity in the design process. The method is more likely to achieve a design solution that combines systematicity, foresight, rationality, and aesthetics, and provides an empirical case for the application of data simulation in public space, aiming to improve the rationality of public space design and solve the data-objective problems faced by the design of public space at this stage.

Swpmmas: an optimized parallel max-min ant system algorithm based on the SW26010-pro processor

Swpmmas: an optimized parallel max-min ant system algorithm based on the SW26010-pro processor

An Improved Ant Colony Clustering Algorithm and Application in Classification of Tree Species

Abstract Clustering is one of the most challenging research topics and has attracted the attention of many researchers. Clustering aims to divide a given set of objects into several categories according to certain rules. It is widely applied to practical problems, such as knowledge discovery, artificial intelligence, scientific data analysis, business, biology, medical diagnosis, text mining, and web data mining and so on. Ant colony clustering algorithm (ACCA), which integrates ACA into clustering, is an efficient swarm intelligence algorithm. There are some weakness in the basic ant colony clustering algorithm, such as low solving efficiency, poor calculation accuracy and iteration stagnation. In this article, an improved Ant Colony Clustering Algorithm (IACCA), in which novel picking-up and dropping strategy is introduced, is proposed and applied to classification of tree species. The experiments indicate efficiency of IACCA for classification of tree species.

Research on the Design of Performance Evaluation System for Physical Education Major Based on Multimedia Information Processing Technology

The Massive Open Online Course (MOOC) platform, despite its reach and potential, faces significant challenges related to learner diversity, including variations in student backgrounds, work experiences, and learning environments. By integrating the principles of ant colony clustering algorithms with MOOC platform dynamics, our study devises a strategy for grouping students based on shared characteristics, thereby fostering a tailored learning environment. A streamlined MOOC platform design framework is outlined, accompanied by a detailed blueprint for a peer-evaluation information system, aimed at accurately gauging students' academic progress within their disciplines. This platform enables students to effortlessly access and engage with courses aligned with their interests, while the reciprocal assessment mechanism heightens understanding of course materials and alleviates pressure on educators. Our research contributes a pragmatic solution to the challenge of student assignment clustering within MOOCs, enhancing both individual learning outcomes and the broader instructional quality.

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.

Segmentation of image from a first-person-view unmanned aerial vehicle based on a simple ant algorithm

The object of this study is the process of image segmentation from the First Person View (FPV) of an unmanned aerial vehicle (UAV). The main hypothesis of the study assumes that the use of a simple ant algorithm could ensure the necessary quality of the segmented image. The segmentation method, unlike the known ones, takes into account the number of ants in the image, weight, initial amount and evaporation rate of the pheromone, the "greediness" of the algorithm and provides: – preliminary selection of individual channels of the Red-Green-Blue (RGB) color space; – preliminary placement of ants according to the uniform law; – determining the routes of ants; – taking into account the attractiveness of the route for each ant; – change (adjustment) in the concentration of ant pheromones; – calculation of the probability of movement (transition) of the ant on the movement route; – determination of the objective function at the j-th iteration and its minimization; – determining the coordinates of the route of movement (movement) of ants; – verification of the fulfillment of the stop condition; – determination of the best routes found by ants; – calculation of the brightness of the pixels of the segmented image in each channel of the RGB color space; – further combining the results of channel segmentation. An experimental study of image segmentation from UAV FPV based on a simple ant algorithm was conducted. The specified object of interest on the segmented image has a certain structure, unevenness of the contours, and can be further used for decoding, categorization, etc. Unlike the object of interest, the background ("garbage" objects) in the segmented image do not have a stable structure and can be further filtered out. It has been established that the segmented image by the known method based on the gradient module has a low contrast value, there are gaps in the segmented pixels of the object of interest. A segmented image using a method based on a simple ant algorithm is free from that drawback.

ECG arrhythmia classification based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability

Electrocardiograph (ECG) is one of the most critical physiological signals used for arrhythmia diagnosis. In recent years, ECG arrhythmia classification devices consisting of multi-module sensors, clustering algorithms and neural networks play an important role in monitoring and diagnosing cardiovascular diseases. However, the commonly used ECG arrhythmia classification methods are still facing some problems such as the complex model structure and long running time. To address the above problems, this paper proposes an ECG arrhythmia classification method based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability (SFP-FACC), which uses LSTM to fit the cluster centers and avoids the time consumption of updating the cluster centers during the classification process. The spatiotemporal feature perception ability of this model with the dynamic time warping (DTW) algorithm is improved. The classification is achieved by applying the combination of Euclidean distance and DTW. The convergence speed of the model is improved by using dynamic pheromone volatility coefficient; and finally the optimal solution of the model is determined by using radix sort. Based on the MIT-BIH arrhythmia dataset, the overall accuracy of the proposed classification method in this paper achieves 99.04 %, and even the accuracy of certain types of classification achieves 100 %, and the running time is about 3.5 times faster than that of the basic models. The experiments show that the method proposed in this paper has certain advantages.

Customised bus service design considering flexible vehicle size and transfer incentivization

In this study, the flexible vehicle size and transfer incentivization strategies are incorporated to increase the potential of the customised bus system. In detail, the modular vehicle system is adopted for flexible vehicle size design since it can be connected as the assembled bus in response to demand variation. Also, the transfer among buses is considered to group passengers with the same destinations to increase bus utilisation. However, the passengers will not actively transfer as it is viewed as a disutility. Thus, the incentivization is provided and the transfer demand is elastic under incentivization. We jointly optimise passenger-route assignment, vehicle size, and incentivization scheme by transforming the original nonlinear programming model into a mixed integer linear programming model. Then, to solve the larger case study, the modified ant colony system algorithm is also proposed. The experiments validate the superiority of customised bus service considering flexible vehicle size and transfer incentivization.

Методика для решения задачи выбора признаков в регрессионной модели Кокса

The technique based on the use of a hybrid optimization method to solve the feature selection problem for the Cox regression model is proposed. The hybrid optimization method includes two metaheuristic methods: the ant colony optimization and the genetic algorithm. The ant colony optimization used as the basic algorithm that solves the main optimization problem. The genetic algorithm solves the problem of finding the optimal set of parameters for the ant algorithm improving its performance. The method is modified and adapted to solve the problem under consideration. The key feature of adaptation is the deposition of pheromones on the vertices rather than on the edges of the graph, as well as the method for calculating the assessment of heuristic information about each vertex. A fitness target function was constructed that determines the quality of solutions to the feature selection problem and is based on an assessment of the performance of the Cox model with a selected set of parameters. The concordance index (c-index) was used to evaluate the Cox model. The efficiency of the methodology is shown using the implemented program using the example of a database of recidivism. For the database used, the most significant sets of features were obtained that have the greatest impact on the quality of training of the survival analysis model.

High-speed 3D printing temperature system with optimized PlD parameters based on improved ant lion optimization algorithm

An Ant Lion Optimization algorithm based on elite Opposition-based learning and Cosine factors (OCALO) is proposed to address the problem of poor response and stability during heating process in high-speed 3D printing temperature system.The generation of the initial solution in OCALO algorithm is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping, which guarantees the diversity of population. The PID parameters are optimized using the improved algorithm. Compared with two existing classical algorithms and three improved ALO algorithms, the proposed algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The outcomes of simulation and experimentation demonstrate that the algorithm improves the transient and steady-state performance of temperature control with better accuracy and robustness. It takes at least 123 s faster than other controllers to reach stability and is more than two times stronger than other PID controllers, making it better suited to high-speed 3D printing temperature systems.

Optimization of the Colony Size Parameter in the Ant Algorithm for Solving the Routing Problem in Communication Networks

With the increasing amount of information and complexity of communication networks, the issue of routing remains relevant. The routing problem is a classic problem of building a logical structure of a communication network. To solve this problem, many methods are used, such as: genetic algorithms, ant algorithm, swarm intelligence, artificial bee colony algorithm, firefly algorithm, bacterial food search optimization algorithm, etc. Despite the fact that the effectiveness of using the ant algorithm is widely known and described, however, there is no single approach to finding the optimal use case for the algorithm. There is a need to determine the optimal size of the colony when solving the routing problem. The article describes the results of the simulation process of modeling the behavior of ant colonies of different sizes to find the optimal route through the AnyLogic environment. It is determined that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks. To find the optimal ratio of the colony size to the complexity of the task, you can use the grid search methodology. The results clearly demonstrate that with increasing complexity of the task (the number of communication nodes), the same number of ants cope worse with the task. The problem associated with the need to accurately determine the optimal size of a colony is of great practical importance, because an increase in the size of a colony has generally decreasing utility. It was found that for many, an increase in the number of ants from 100 to 500 had a relatively greater effect than an increase in the size of the colony from 1000 to 10000. It is proved that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks.

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

Quantum-inspired firefly algorithm with ant miner plus for fake news detection

Nowadays, technology has shifted the way individuals access news from conventional media sources to social media platforms. The active engagement of people with social media platforms leads them to consume news without confirming its source or legitimacy. This facilitated the dissemination of more manipulated and false information in the form of rumors and fake news. Fake news can affect public opinion and create chaos and panic among the population. Thus, it is essential to employ an advanced methodology to identify fake news with high precision. This research work has proposed the concept of the quantum-inspired firefly algorithm with the ant miner plus algorithm (QFAMP) for more effective fake news detection. The proposed QFAMP algorithm utilizes the attributes of quantum computing (QC), the firefly algorithm (FA), and the ant miner plus algorithm (AMP). Here, the QFA approach ensures the effective exploitation of the firefly agents until the agents are able to search for the brighter firefly. Further, the AMP algorithm utilizes the best ants with higher pheromone concentrations for global exploration, which also avoids the premature convergence of the QFA agents. In addition, the AMP algorithm serves as an efficient data mining variant that is effective for the classification of fake news. The efficacy of the proposed QFAMP algorithm is evaluated for the dataset of FakeNewsNet, which is composed of two sub-categories: BuzzFeed and PolitiFact. The experimental evaluations indicate the effective performance of the proposed algorithm compared to the other techniques.

Isolated Random Forest Assisted Spatio-Temporal Ant Colony Evolutionary Algorithm for Cell Tracking in Time-Lapse Sequences.

Multi-Object tracking in real world environments is a tough problem, especially for cell morphogenesis with division. Most cell tracking methods are hard to achieve reliable mitosis detection, efficient inter-frame matching, and accurate state estimation simultaneously within a unified tracking framework. In this paper, we propose a novel unified framework that leverages a spatio-temporal ant colony evolutionary algorithm to track cells amidst mitosis under measurement uncertainty. Each Bernoulli ant colony representing a migrating cell is able to capture the occurrence of mitosis through the proposed Isolation Random Forest (IRF)-assisted temporal mitosis detection algorithm with the assumption that mitotic cells exhibit unique spatio-temporal features different from non-mitotic ones. Guided by prediction of a division event, multiple ant colonies evolve between consecutive frames according to an augmented assignment matrix solved by the extended Hungarian method. To handle dense cell populations, an efficient group partition between cells and measurements is exploited, which enables multiple assignment tasks to be executed in parallel with a reduction in matrix dimension. After inter-frame traversing, the ant colony transitions to a foraging stage in which it begins approximating the Bernoulli parameter to estimate cell state by iteratively updating its pheromone field. Experiments on multi-cell tracking in the presence of cell mitosis and morphological changes are conducted, and the results demonstrate that the proposed method outperforms state-of-the-art approaches, striking a balance between accuracy and computational efficiency.

ANALYSIS OF THE SHORTEST ROUTE FROM CIKAMPEK TO PURWOKERTO USING ANT COLONY ALGORITHM

Finding the shortest path sometimes makes most people confused in choosing a path, especially if there are many paths that must be taken to reach one destination. Tracing various routes from Cikampek to Purwokerto is a challenge, with the risk of spending excessive time and money. To solve this problem, this research uses the Ant Colony Algorithm to determine the most efficient path. One of the problems that often arises is that the Cikampek area has 3 routes that can be used to reach Purwokerto, namely the Cikampek - Indramayu (Patrol), Cikampek - Subang, and Cikampek - Bandung routes. The purpose of determining the shortest path is to provide the shortest path solution to users so that they can reach their destination faster and save time and costs. In this study, we use Ant Algorithm because the route from Cikampek to Purwokerto depends on many road factors and other conditions that could change from time to time. For example, traffic congestion and road repairs are major factors. The ant algorithm has strong customization capabilities to determine the shortest path based on the pheromone trails left by ants during exploration. This means that the ant algorithm can work optimally even if there are many factors in road conditions. In addition, the ant algorithm has the ability to handle problems with a large number of nodes, so it can explore various possibilities efficiently. The ant algorithm is an appropriate choice for finding the shortest path in a journey involving complex and diverse routes. There are six steps required to complete the ant algorithm. The first step is initialization, the second step is path selection, and the third step is setting the ant trail and communication between the ants hit by the trail in path selection. The fourth step is to look for visibility, the fifth step is to stop the criterion until it reaches a certain condition, and the last step is to determine the path by calculating probabilities. The results of this study indicate that the ant algorithm can be used to determine the shortest path which is then displayed. This research aims to assist the community in making decisions and determining the location of the city that must be passed. This study took 8 location points with starting point A and destination point H. Based on the results of the discussion it can be concluded that the shortest route from Cikampek to Purwokerto is with 3 routes, namely first (A à D à E à F à H) with a distance of 355 KM, second (A à D à G à F à H) with a distance of 431 KM and the third (A à D à E àH) with a distance of 317 KM.

Ant algorithms for finding weighted and unweighted maximum cliques in d-division graphs

This article deals with the problem of finding the maximum number of maximum cliques in a weighted graph with all edges between vertices from different d-division of a graph with the minimum total weight of all these cliques, and the problem of finding the maximum number of maximum cliques in a nonweighted graph with not all edges between vertices from different d-division of the graph. This article presents new ant algorithms with new desire functions for these problems. These algorithms were tested for their purpose with different changing input parameters, the test results were tabulated and discussed, the best algorithms were indicated.

Virtual monitoring method for computer network security in earth observation communication using multi ant colony random walk classification algorithm

Virtual monitoring method for computer network security in earth observation communication using multi ant colony random walk classification algorithm

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

Minimisation of the Energy Expenditure of Electric Vehicles in Municipal Service Companies, Taking into Account the Uncertainty of Charging Point Operation

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software.