Ant Colony Method Research Articles

Lifecycle product inventory optimization: a hybrid approach with Grey-Wolf and Ant-Colony methods

Lifecycle product inventory optimization: a hybrid approach with Grey-Wolf and Ant-Colony methods

Analysis and evaluation of the effectiveness of program codes for particle swarm and ant colony methods for lower semi-continuous functions

In this paper, a comparative analysis of two stochastic methods of global optimization is carried out: the Particle Swarm Optimization method and the Ant Colony Optimization method. The analysis and evaluation of efficiency were carried out using parameters such as the number of calls to the objective function, the rate of convergence, resources, computational efficiency, sensitivity to parameters, and resistance to initial conditions. The article presents the program codes of these methods in the Python environment, designed to calculate the global extremum of semi-continuous functions from below. Well- known test functions were used to construct (by gluing) semi-continuous functions at different values of input parameters.

Разработка и применение многокритериального метода муравьиных колоний в задаче оптимизации инвестиционного портфеля

<p>A numerical method of multi-objective optimization is proposed for an approximate solution of the problem based on the generation of feasible solutions using the continuous ant colony method, non-dominated sorting and the epsilon-constraint technique. Solving a problem means finding the Pareto front. Solutions of typical model examples are given. The applied problem of optimizing an investment portfolio has been solved, in which the initial data are the tabulated average returns and covariance of stocks.</p>

Exploring the practice of integrating innovation and entrepreneurship teaching in visual communication design professional courses using hybrid optimization based ANFIS

The convergence of visual communication design with unique effects, graphic design, as well as virtual reality, which is becoming progressively more popular, has created a new paradigm for education in recent years. However, emerging evidence indicates that their integration into the world of learning is a somewhat gradual and intricate process. The present research proposes a novel algorithm and a functional model of artificial intelligence technology design to automatically arrange graphic language in visual communication design. In visual communication design, the goal orchestration function used to determine the display size of buffer images is the difference between the minimum and maximum values of the number of orchestration screens. An ant colony method is used in visual communication design to identify the optimal locations for visuals to be presented, and ASM semantics is used to characterize the visual languages. In order to accomplish the invention and development of a visual communication design style, the suggested algorithm has to be programmed and executed. It employs sequential decision marking to characterize the visual vocabulary and accomplishes automated organization. According to the trial results, visual saturation based on AI technology can reach up to 97%, and the average user satisfaction score is 7.65. It is evident that a creative visual thinking approach can maximize the visual communication design effect and communicate fresh design concepts.

Optimization of parametric problems with a negative value of the objective function using the ant colony method

A modification of the ant colony method is considered, which makes it possible to search for a rational (optimal) solution to a parametric problem with the possibility of obtaining a negative value of the objective function. The objective function in the original algorithm that calculates the traveling salesman's path is determined by the total length of the path and cannot be negative. But when solving parametric problems of optimization of difference values for high availability systems, negative values of the objective function may arise. Such values lead to a negative value of the weights (pheromone), which leads to incorrect operation of the probabilistic arc/vertex selection formula. A formula has been derived for recalculating the positive shift of the objective function and pheromone to obtain a positive value of the pheromone, which is used when the sum of the value of the objective function and the shift is negative. The approaches are compared: initial shift, single shift, calculation of the module of the objective function, and a gradual iterative shift algorithm is proposed. The proposed approach allows us to equate the minimum value of the objective function to 0 and mathematically recalculate the weights (pheromone) for the parametric problem taking into account the resulting shift. By applying relative values of the weights, such a shift does not affect the probability distribution, but takes into account new values for previous calculations of the objective function. The studies were carried out on various benchmarks with optimal values in the negative region. Statistically, the proposed algorithm is not inferior in performance to shift and modular algorithms for converting the value of the objective function to the positive semi-axis.

ПОИСК АЛЬТЕРНАТИВНЫХ ЛЕКАРСТВЕННЫХ СРЕДСТВ С УЧЕТОМ РИСКОВ ПОЛИФАРМОКОТЕРАПИИ МЕТОДОМ МУРАВЬИНЫХ КОЛОНИЙ

This paper considers the problem of simultaneous prescription of a large number of drugs to a patient and consideration of possible risks of polypharmocotherapy. At high risk of polypharmocotherapy it is necessary to revise the prescribed drugs, consider analogs, change the method of administration or reduce the dosage, which requires high qualification and knowledge of the doctor. With automatic risk prediction, it is possible to automate the process of searching for a rational combination of drugs, taking into account dosages and methods of administration. To automate the process, a modified ant colony method is presented in this paper

Exploring the innovation of visual communication design style based on artificial intelligence technology

Abstract By exploring the application of artificial intelligence technology in visual communication design, the design efficiency and innovation of visual communication can be effectively improved. In order to automatically arrange graphic language in visual communication design, a novel algorithm is proposed in this research, together with a functional model of artificial intelligence technology design. The minimum and maximum values of the number of orchestrated pages are used as the objective orchestration function for calculating the display size of buffer images in visual communication design. In visual communication design, an ant colony method is used to determine where graphics should be displayed best, and ASM semantics is used to describe the graphics language. The proposed algorithm is programmed and runs to achieve the innovation and development of visual communication design style by using the parallel selection mark to describe the graphic language and realize the automatic arrangement. The experimental results show that the visual saturation obtained based on artificial intelligence technology can reach up to 97%, the mean value of user satisfaction is 7.789, and the TGB consideration characteristics are evenly distributed. It can be seen that an innovative visual thinking mode can convey novel design concepts in visual communication design and optimize the visual communication design effect.

Collaborative path planning and task allocation for multiple agricultural machines

Path planning and task allocation are critical concerns in multi-machine collaborative operations for unmanned farms. Nevertheless, several problems remain in the operation of agricultural machinery, such as the slow path planning algorithm, the omission of the working area, and the unreasonable scheduling of machines, resulting in low efficiency and wasted resources. Collaborative and complete coverage path planning was achieved to solve the problems of slow path planning algorithms and the omission of working areas. The farm’s electronic map was constructed using the topological map method. The improved Dijkstra algorithm based on priority queues was combined with three different complete coverage methods: the nested method, the reciprocating method, and the combination of nested and internal spiral path methods. The simulation results show that the improved Dijkstra method based on priority queues can effectively minimize the running time of the algorithm. The reciprocating method has a higher coverage index than the other two methods, with an average coverage rate of 94.73 %. To solve the problem of illogical scheduling of the same type of agricultural machines, an improved ant colony method was presented based on the whole working path to minimize the path cost. The simulation results show that the proposed method can allocate the task properly, and the path cost is reduced by 14 %–33 %. By combining the proposed path planning and task allocation methods, the whole-process path planning of a single agricultural machine and multiple agricultural machines of the same type was achieved, providing a technical solution for promoting the construction of unmanned farms.

Dynamic Route Optimization for Chinese E-Commerce Logistics Based on Ant Colony Algorithm

Abstract The limited nature of logistics and distribution vehicles and the variability of customer acceptance service time limit the service efficiency and quality of logistics and distribution. To optimize a logistics distribution path under capacity and time window constraints, a mathematical model of the problem is first developed in this study, with the lowest cost as the model’s objective function. The logistics distribution issue with soft time windows is then addressed using an ant colony algorithm, and a logistics path optimization strategy based on the maximum minimal ant colony system is suggested. Then, the heuristic function is rebuilt to improve the ant colony algorithm’s solution speed, and the pheromone update approach is included. Finally, experimental approaches are used to test the model’s and optimization algorithm’s efficacy for customer sizes of 30, 50, and 100. The experimental results show that the optimized ant colony algorithm has the best value of 2 for α and 3 for β, which can converge earlier. The improved ant colony algorithm also finds the best solution faster than the conventional ant colony method in just 23 rounds. In the mathematical model of the logistics distribution path optimization issue, this study suggests that the optimized ant colony method has the optimization algorithm’s rationality, efficacy, and stability.

Control of Set of System Parameter Values by the Ant Colony Method

Control of Set of System Parameter Values by the Ant Colony Method

New Ant Colony Algorithm for Solving Partial Job Shop Scheduling Problem

Scheduling problems are one of the most researched topics in the field of operational research. Scheduling problem models have evolved and branched because of the wide range of products, standards, and customer requirements. Recently, the partial job shop scheduling problem, which is a general model of shop scheduling problems, has become a new scheduling problem. Operations in this model are partially ordered, and the order varies for each job. Several problems studied independently in the literature, such as the group shop, mixed shop, job shop, and open shop scheduling problems, are considered special cases of the partial shop scheduling model. Because ant algorithms are known in the literature as effective tools for solving combinatorial optimization problems, this study proposed an ant colony (AC) algorithm for solving partial shop problems with an objective function to minimize makespan. The AC method was examined and evaluated on the renowned “Taillerd” benchmark instances. It was then compared with the hybrid scatter search (HSS) and iterated tabu search (ITS) methods. The AC algorithm’s average deviation for 80 instances ranged between 0% and 1.78%. The AC algorithm outperforms the HSS and ITS methods, according to the computational findings; where the average percentage relative deviation for AC is 0.66%, compared with 0.99% for ITS and 10.14% for HSS.

Development of an Algorithm for Solving an Asymmetric Routing Problem Based on the Ant Colony Method

Development of an Algorithm for Solving an Asymmetric Routing Problem Based on the Ant Colony Method

Títul Mobile visual media design based on digital media technology and augmented reality

Abstract The reliance on shape-based parameters for traditional 3D multimedia visual behavior can significantly reduce the stability of image simulation. In this paper, the video feature enhancement method of wavelet and human eye vision is used to set the initialization parameters of the ant colony method calculation and calculate the video pixel to different food source distances as a way to obtain the amount of information of each path. The interface pixel affiliation is calculated to make a judgment on whether the amount of information is greater than the initial threshold. The contrast between the detail components of the interface at different scales is enhanced using the wavelet multistage method, and the video visual feedback enhancement is achieved by extracting the interface edge regions in each scale through the local shareholding method. Then the multimedia video representation of human behavior as silhouette sequence, extract the human behavior motion trajectory as large-scale multimedia visual behavior features, and extract the edge features of multimedia vision, and express the results as visual detail features in multimedia video. Finally, a multimedia visual human behavior state model is formed, and the patio-temporal sequence of the model is estimated by fusing it with particle filtering approximation inference to complete the behavioral simulation analysis of multimedia vision. The experimental results show that the average recognition rate of the method in this paper is as high as 98%, the accuracy of behavioral feature extraction is as high as 96%, and the error rate of modeling is 0.02%.

A study on path optimization of construction machinery by fusing ant colony optimization and artificial potential field

AbstractWith the progress of manufacturing capability and the requirements of modern project construction clustering and safety, unmanned technology has become a major development direction for construction machinery. The study analyzes the defects of the ACO applied to path planning, uses the basic ant colony method combined with the APF algorithm to construct comprehensive heuristic information to improve the efficiency of the initial iteration, and adds a pseudo‐random proportional transfer rule to improve its transfer rule, in addition to proposing an optimal ant pheromone release rule to ensure that the pheromone update can effectively improve the effect of the later iteration. In the 20 × 20 raster map, the optimal path of both the basic and potential field ant colony algorithms is 31.52, while the basic ant colony algorithm does not converge to the optimal path after 200 iterations in the 30 × 30 raster map, and the comparison algorithm converges to the optimal path after 156 iterations, while the potential field ant colony algorithm converges to the optimal path after 25 iterations at 47.52. And the potential field ant colony The potential field ant colony algorithm converges to the optimal path of 47.52 after 25 iterations, and the number of turns in both raster maps of different sizes is only about 50% of the basic ant colony algorithm, which is also better than that of the comparison algorithm. The proposed algorithm improves the ability of the ant colony algorithm to explore and exploit raster maps, enhances its path planning capability, and improves the construction efficiency well in practical applications.

Inter-Object Navigation of Unmanned Aerial Vehicles to Increase the Efficiency and Accuracy of Inspection of Power Lines

The purpose of the work is to improve the accuracy and efficiency of a power line section inspection for a fault detection using unmanned aerial vehicles. The goal was achieved by using a unified computing and measurement platform on multicopter and aircraft drones and by simplifying the interaction between them and by using the inter-object navigation sensors. The most significant results were the development of a method of route planning by drones over different parts of the power grid and a method of inter-object navigation. The drone route planning problem was represented by a multiagent variation of the classical traveling salesman problem and was solved by the ant colony method. The method of inter-object navigation was distinguished by the representation of the power grid topology by high and low intensity graphs, involving a different number and types of drones in the inspection process. The application of the developed methods made it possible to increase the accuracy of power line inspections by 27-73%, and the efficiency by 2-8 times. Solving the problem of multicriteria optimization of the drone team flight route planning made it possible to reduce the cost of monitoring critical infrastructure facilities while improving its efficiency and accuracy. Thus, the conducted research has shown the effectiveness of the proposed approach for the monitoring of power facilities, route selection, number and composition of search teams. The direction for further research is to improve the ant algorithm.

Optimal Power Flow For Non-Smooth Cost Function Using Particle Swarm Optimization On 150 Kv System

Optimal power flow by considering the non-smooth cost curve using the meta-heuristic algorithm method, namely particle swarm optimization (PSO) in the 150 kV Sulselrabar electrical system. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve and still considered the limitations of similarity and inequality. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve and still considered the limitations of similarity and inequality . From the results of generation optimization using the Particle Swarm method, it produces the cheapest generation costs from other methods, namely Rp. 93,498,916.1,- / hour to generate power of 270.14 MW with losses of 25.73 MW. The Particle Swarm Optimization (PSO) method is able to reduce the cost of generating the Sulselrabar system by Rp. 34,382,857.58 / hour or 26.89%. From the results of generation optimization using the Ant Colony method, it resulted in a total generation cost of Rp. 94670335.98 / hour to generate power of 270,309 MW with losses of 25.91 MW. The Ant Colony method is able to reduce the cost of generating the Sulselrabar system by Rp. 33,211,437.70 / hour or 25.98%. From the results of generation optimization using the lagrange method, it resulted in a total generation cost of Rp. 117,121,631.08 / hour to generate power of 339.4 MW with losses of 25,016 MW . The lagrange method is able to reduce the cost of generating the Sulselrabar system by Rp. 10,760,142.60 / hour or 8.41%. The artificial intelligence method based on Particle Swarm Optimization (PSO) can well perform optimization of Optimal Power Flow, from the results of the analysis obtained the cheapest generation cost compared to the comparison method, Lagrange Method and Ant Colony artificial intelligence method .

Optimization of emergency supplies paths based on dynamic real-time split deliver

A multi-objective dynamic demand split delivery emergency material distribution model is developed to enhance the efficiency of emergency material distribution and facilitate the smooth progress of safety rescue operations during unconventional emergencies. This model incorporates the psychological view of those affected by disasters. The issue of dynamic demand may be transformed into a static demand problem by dividing the distribution time window into time domains of equal length. The optimization process is thereafter executed in real-time with the timed batch methodology. A refined ant colony method has been developed to address the model by integrating the attributes of the mathematical model, followed by doing an arithmetic case analysis. The findings indicate that the algorithm and mathematical model suggested in this study are efficacious in addressing the emergency material distribution issue, offering valuable decision-making advice and reference.

Joint Virtual Machine Selection and Computation Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) is considered as an effective technology to enhance the storage and computation capability of smart power sensors (SPSs) in smart grid networks. The MEC server is composed of multiple virtual machines (VMs) with powerful computation capability, and each VM can process multiple tasks independently, which cannot be ignored during the task computation period. In this work, we aim to minimize the energy consumption of SPSs subject to the task offloading delay by jointly optimizing the VM selection and computation resource allocation. Considering the formulated problem is nonconvex, we first utilize the linearization method to transform it into a convex optimization problem. And then, by using the branch and bound method, we propose the joint VM selection and computation resource allocation (JVMSRA) algorithm. Considering the complexity of the JVMSRA algorithm is high, we decompose the primal problem into two subproblems and solve them by utilizing the ant colony method and CVX package, respectively. Based on the solutions of the two subproblems, the resource allocation‐based ant colony (RAAC) algorithm is proposed. Simulation results show that the proposed RAAC algorithm and JVMSRA algorithm decrease by 6% and 8.8% on average compared with the benchmark algorithm, respectively, when the computation resources of each VM increase from 1 to 3GHz.

Multi-strategy hybrid heuristic algorithm for single container weakly heterogeneous loading problem

Three-dimensional single container weakly heterogeneous loading problem is one of the most classical tasks which has various applications in manufacture and logistics industry. Solving this problem could improve transportation efficiency to bring great benefit to shipping customers. During the last two decades, many heuristic, meta-heuristic and hybrid algorithms have been proposed to maximize container volume utilization to reduce the waste of container space significantly. Despite their success in many real-world applications, it is still a challenging task to recommend satisfactory loading levels within a limited time frame when clients approach for options of different combinations of shipping items. In this paper, we propose a novel multi-strategy hybrid heuristic algorithm to achieve timely planning for clients in a required short time frame. In specific, a probabilistic model is used to combine the strength of two optimization strategies, i.e. an ant colony method and a constructive greedy method, to speed up the optimization process and ensure better convergence. In addition, a tree pruning strategy is designed to further improve the efficiency of the hybrid heuristic algorithm. Extensive experiments demonstrate the effectiveness of our method in terms of both volume utilization rate and algorithm processing speed compared to state-of-the-art methods. Based on the comparison results by using BR dataset, we achieved averagely 94.31% volume utilization rate and 50.16 s processing speed, which is the best performance by considering both algorithm effectiveness and efficiency. Further, our proposed method has been deployed in a real business case to provide plan solutions to individual customer shipping requests and achieved high customer satisfaction rate.

Optimization of the order of hyperparameters of computational cluster by the ant colony method

Optimization of the order of hyperparameters of computational cluster by the ant colony method