Initial Pheromone Distribution Research Articles

Research on Modeling Method for Optimal Allocation of Wellhead Targets in Large Well Clusters

The paper proposes a genetic ant colony algorithm that integrates genetic and ant colony algorithms, enhancing the heuristic function of the latter, to address target point distribution issues in large well clusters. This algorithm utilizes genetic algorithms for initial pheromone distribution and employs the ant colony algorithm to achieve rapid convergence. Introducing genetic operators in each iteration addresses the ant colony system’s drawbacks, including scarcity of initial pheromones, susceptibility to local optima, and slow convergence speed. The model aims to minimize the sum of horizontal displacement and intersections in line connections from wellheads to target points as its dual-objective function. It validates the effectiveness of the genetic ACO algorithm in optimizing target point allocation at wellheads through a case study, highlighting its advantages over traditional methods in reducing displacement, ensuring result stability, and preventing collisions.

Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem

The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.

Hybrid ant colony and intelligent water drop algorithm for route planning of unmanned aerial vehicles

Route planning is a crucial element in unmanned aerial vehicle (UAV) systems, particularly in autonomous UAV technology. In the past decades, various algorithms have been proposed for UAV route planning. However, they still have defects, such as stagnation and slow search rates. In this study, a novel hybrid algorithm which integrates ant colony optimization (ACO) and intelligent water drop (IWD) is proposed for UAV route planning. First, the advantages of the IWD and ACO algorithms are combined in an iterative strategy, to ensure mutual cooperation via exchange of information for route optimization. Initially, the water drops optimize the soil and pheromones within the environment simultaneously to generate good approximate solutions and an initial pheromone distribution for the ant colony. Based thereupon, the ant colony roams the solution space to further optimize the routes, thus combining the exploration potential and exploitability of two types of agents. Additionally, a novel node selection strategy is proposed to guide the agents’ route planning along a reasonable direction. Compared with state-of-the-art algorithms, the convergence accuracy, success rate, and stability of the proposed algorithm exhibited significant improvements of approximately 8.25%, 4.20%, and 66.20%, respectively.

Modeling Analysis of the Relationship between Adolescent Aerobic Exercise and Obesity Reduction Based on Deep Learning

In order to explore the modeling analysis of the relationship between adolescent aerobic exercise and obesity reduction, the relationship modeling method of deep learning algorithm is proposed. This study integrates deep learning algorithms and first uses the changes in body shape, weight, BMI index, body fat, body circumference, and other indicators of adolescent obese before and after aerobic exercise as the initial pheromone distribution matrix and introduces random evolution factor and evolutionary drift threshold to establish the objective function of aerobic exercise to reduce adolescent obesity. It also explains the constraint conditions that aerobic exercise has to meet in the reduction of adolescent obesity and introduces particle algorithm to establish a model of optimal aerobic exercise to reduce adolescent obesity. The simulation results show that, under the same number of experiments, the advantages of this method are more obvious. On the overall level, the average modeling error of this method is about 0.053%, while the average of the traditional method is about 0.186%, indicating that the method can reduce the error control within a reasonable range. It proves that deep learning can effectively reflect the modeling analysis of the relationship between adolescent aerobic exercise and obesity reduction.

Modeling and Analysis of the Relationship between Aerobic Exercise and Obesity Reduction in Adolescents.

In order to solve the relationship between youth aerobic exercise and obesity reduction, an improved ant colony algorithm-oriented aerobic exercise method was proposed. Firstly, the changes in body shape, weight, BMI, body fat, body circumference, and other indicators of obese adolescents before and after aerobic exercise were used as the initial pheromone distribution matrix, and the random evolution factor and evolutionary drift threshold were introduced to establish the target function of reducing obesity caused by aerobic exercise in adolescents. The constraint conditions of the relationship between aerobic exercise and adolescent obesity reduction were explained, and the particle algorithm was introduced to establish the optimal model of aerobic exercise for adolescent obesity reduction. The experimental results show that with the increasing number of experiments, the advantages of this method are more obvious. From the overall level, the average modeling error of this method is about 0.053%, while the average error of the traditional method is about 0.186%, which shows that this method can control the error within a reasonable range, and it is proved that the improved ant colony algorithm can have a good correlation with the method of aerobic exercise.

An RFID-Enabled IoT-Based Smart Tourist Route Recommendation Algorithm

With the rapid development and broad deployment of Internet of Things (IoT) technologies, the IoT are increasingly shifting away from “interconnection of everything” to “human-computer-thing” sensing integration. Although there are numerous sensing technologies available today, radio frequency identification (RFID) has emerged as useful medium for “passive sensing” due to its lightweight, taggable, and simple deployment properties. With the growth of social networks in recent years, it has become a significant research hotspot for the development of path suggestion systems that are tailored to the demands of individual users’ preferences. This paper considers the relevant features of interest points, integrates the user’s emotion and product similarity into the heuristic function of the ant colony algorithm, adopts the elite management ant strategy, maximizes the management ant strategy, and uses particle swarm algorithm to improve the initial pheromone distribution of the ant colony algorithm. The proposed model combines the ratings of 593 tourists and text comment information into one dataset and proposes a smart tourist route recommendation model. The improved ant colony algorithm is utilized to recommend the most popular tourist routes and recommend the tourist routes of the most popular tourist spots in the scenic area. The suggested method is more efficient in terms of accuracy and recall. The F measure value is derived from real-world dataset testing.

Radiation avoiding algorithm for nuclear robot path optimization

Improved elephant herding-ant colony hybrid algorithm (IEHO-ACO) is proposed for the nuclear robot path planning problem under the dual constraints of distance and radiation in the radiation environment. In this algorithm, the particle swarm algorithm speed update method and “hybrid operation” are introduced to increase the ability of the elephant herding optimization to jump out of the local optimum. The shortest path obtained by the improved elephant herding optimization is converted into the increment of the initial pheromone distribution of the ant colony optimization to increase the convergence speed of the algorithm and reduce the “blindness” of the ants. At the same time, the “Z” transformation strategy is introduced to dynamically adjust the combination of pheromone heuristic factor and expectation heuristic factor, so as to avoid the occurrence of premature stagnation and falling into local optimum in the search process. Taking full account of the particularity of the radiation environment, the way heuristic factors and pheromone updates in the ant colony algorithm are adapted. Also, the attraction of the target point to the ants is increased when the fallback strategy solves the deadlock problem. The simulation results show that the paths obtained by IEHO-ACO are more reliable and converge faster, and it can be applied to complex environments, especially in environments containing concave obstacles, IEHO-ACO shows good search performance.

A Novel Approach of Image Fusion Techniques using Ant Colony Optimization

Ant Colony Optimization (ACO) is a relatively high approach for finding a relatively strong solution to the problem of optimization. The ACO based image fusion technique is proposed. The objective function and distance matrix is designed for image fusion. ACO is used to fuse input images at the feature-level by learning the fusion parameters. It is used to select the fusion parameters according to the user-defined cost functions. This algorithm transforms the results into the initial pheromone distribution and seeks the optimal solution by using the features. As to relevant parameters for the ACO, three parameters (α, β, ρ ) have the greatest impact on convergence. If the values of α, β are appropriately increased, convergence can speed up. But if the gap between these two is too large, the precision of convergence will be negatively affected. Since the ACO is a random search algorithm, its computation speed is relatively slow.

Research on Three Dimensional Global Path Planning of Unmanned Underwater Vehicle

The ant colony algorithm is used to solve the global path planning problem of unmanned underwater vehicle (UUV) in three-dimensional environment. On the basis of grid model environment, the traditional ant colony algorithm is optimized. In order to meet the needs of path planning, an improved algorithm is proposed to solve the problem of initial pheromone distribution. Simulation results show that the improved ant colony algorithm has the characteristics of fast convergence.

Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment.

This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, based on which a three-step arbitration method is put forward to improve its weakness in global search. For the unknown time-varying dynamic obstacles, an occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment.

Weaving scheduling based on an improved ant colony algorithm

Weaving enterprises are faced with problems of small batches and many varieties, which leads to difficulties in manual scheduling during the production process, resulting in more delays in delivery. Therefore, an automatic scheduling method for the weaving process is proposed in this paper. Firstly, a weaving production scheduling model is established based on the conditions and requirements during actual production. By introducing flexible model constraints, the applicability of the model has been greatly expanded. Then, an improved ant colony algorithm is proposed to solve the model. To address the problem of the traditional ant colony algorithm that the optimizing process usually traps into local optimum, the proposed algorithm adopts an iterative threshold and the maximum and minimum ant colony system. In addition, the initial path pheromone distribution is formed according to the urgency of the order to balance each objective. Finally, the simulation experiments confirm that the proposed method achieves superior performance compared with manual scheduling and other automatic methods. The proposed method shows a certain guiding significance for weaving scheduling in practice.

Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm

In the domain of robotics and autonomous driving, the automatic path planning of vehicle collision-free motion is an essential task on the navigation level. It is found that the traditional path planning algorithm and the ployline path cannot fully meet the driving requirements of Ackerman chassis robot. In order to solve the autonomous navigation problem of Ackerman chassis mobile robot in structured environment, this paper presents a new improved algorithm. The method of configuration space can introduce the robot's own structural size parameters into the algorithm. Through convex polygon detection method, the local U-shaped area in the map is transformed into a closed area. The essence of these two strategies is to preprocess the map. The initial pheromone distribution is no longer globally uniform, but is distributed according to the terrain. The volatilization factor of pheromone is changed from static constant to dynamic one, which is combined with Poisson distribution law. This strategy makes the improved pheromone distribution law not only avoid the randomness and blindness in the initial stage of the algorithm, but also ensure the ant colony's exploration behavior and guiding role in the middle stage of the algorithm. Path smoothing is also a challenging task. This algorithm optimizes the path step by step by improving the evaluation function, removing redundant nodes and 2-turning algorithm. Thus, a collision free smooth path suitable for Ackerman robot is obtained. This paper combines a variety of algorithm improvement strategies, not only improving the performance of ant colony algorithm path exploration, but also planning a smooth curve path suitable rather than polyline for Ackerman mobile robot tracking. The algorithm is coded and simulated by MATLAB, and the feasibility and effectiveness of the algorithm are verified. This will provide an important basis for the subsequent algorithm migration and lay the foundation for the path tracking control of the Ackermann chassis robot.

An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application

In this paper, an effective improved co-evolution ant colony optimisation (MSICEAO) algorithm is presented to solve complex optimisation problem. In the MSICEAO, the multi-population co-evolution strategy is used to divide initial population into several sub-populations to interchange and share information. The weighted initial pheromone distribution strategy is used to improve the efficiency and adjust the pheromone factor and distance factor. The elitist retention strategy is used to improve the solution quality. The adaptive dynamic update strategy for pheromone evaporation rate is used to balance the convergence speed and solution quality. The aggregation pheromone diffusion mechanism is used to enhance the cooperative effect and highlight the cooperative idea of swarm intelligence. In order to verify the effectiveness of the MSICEAO, the experiments have been carried out on eight TSPs and one actual gate allocation problem. The MSICEAO is compared with five state-of-the-art algorithms of TS, GA, PSO, ACO and PSACO. The experiment results demonstrate that the MSICEAO is significantly better than the compared methods.

Research on Hodoop Job Scheduling Algorithms Based on Dynamic Fusion

Adaptive genetic algorithm and improved ant colony algorithm are combined to solve Hadoop job scheduling problem. Firstly, the global search ability of the adaptive genetic algorithm is used to generate the list of resources allocated by the task. When the search speed of the genetic algorithm gradually decreases, the optimal integration time of the adaptive genetic algorithm and the ant colony algorithm is determined dynamically. The initial pheromone distribution of the ant colony algorithm is generated from the optimal solution solved by the adaptive genetic algorithm. Improve the target node selection strategy of ant colony algorithm, consider the success rate of completing tasks of nodes, and accelerate the speed of ant colony algorithm to solve the optimal solution. Simulation results show that compared with genetic algorithm and ant colony algorithm, hybrid genetic algorithm takes less time, and the more tasks, the more obvious advantages.

Study on the Resource Allocation Optimization in Cloud Computing Based on the Hybrid Optimization Algorithm

The methods of solving resource allocation are mainly heuristic algorithms which could not solve resource allocation problems in cloud computing. The hybrid optimization algorithm is studied to solve the problem. There are many different hybrid optimization algorithms. Our research hopes to find a simple and effective method. We select a combination optimization algorithm of the genetic and ant colony algorithms. In the early phase of this algorithm, with the help of the wide range search capabilities of the genetic algorithm, it finds a better solution; in the later stage of this algorithm, with the help of positive feedback and efficiency of the ant colony algorithm, it finds the optimal solution. In addition, the two algorithms convergence conditions and the way of how to make the better solution of the genetic algorithm translate into the initial pheromone distribution provisions of the ant colony algorithm are set up. At last, the algorithm was realized with a simulation environment, and a specific example was made by comparative analysis to verify the correctness and effectiveness of the algorithm

Routing optimization of emergency grain distribution vehicles using the immune ant colony optimization algorithm

The routing optimization problem of grain emergency vehicle scheduling with three objectives is studied in this paper. The objectives are: maximizing satisfaction of the needs at the emergency grain demand points, minimizing total cost of grain distribution and minimizing the distribution time. A hybrid algorithm is present to solve the proposed problem based on combining artificial immune and ant colony optimization (ACO) algorithms. This hybrid algorithm calculates the degree of crowding and conducts non-dominated sorting of the population in the ant colony optimization algorithm by applying a Pareto optimization model. A better solution set is quickly generated by making use of the fast global convergence and randomness of the improved immune algorithm together with the distributed search ability and positive feedback of the ACO algorithm. A better solution set obtained as the initial pheromone distribution, is solved further by using ACO until the approximate optimal solution set is obtained. A comparison of the proposed algorithm with several common optimization algorithms on the Solomon benchmark dataset demonstrates that this method obtains better performance in shorter time, and is an efficient way to solve the vehicle routing problem in emergency grain distribution scenarios.

Path Planning Based on an Improved Ant Colony Algorithm

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

A Cloud Computing Resource Scheduling Method Based on Particle Swarm Optimization and Ant Colony Optimization

Cloud computing system needs to process a large amount of data and has arduous tasks. An important question in cloud computing research is how to scheduling the resource rationally in this vast system. This paper presents an algorithm of resource scheduling combining Particle Swarm Optimization and Ant Colony Optimization. Firstly, get a good initial solution quickly through PSO algorithm, then ACO use the initial solution to determine the generation of initial pheromone distribution which is used to calculate the optimal solution through iteration. By experiment, the scheduling algorithm which combines PSO and ACO is better than both of the algorithms based on PSO or ACO.

An improved particle swarm-ant colony hybrid algorithm for HMM training

The traditional parameter estimation methods of Hidden Markov Models (HMM) are easy to fall into local optimum, have higher requirements for initial parameter values and might result in over-coupling phenomena. In order to improve the robustness and identification performance of the model, a novel HMM parameter training method based on an improved particle swarm-ant colony algorithm (IPSAA) is presented. First, extremum disturbance is added into particle swarm optimisation algorithm (PSO) and parameters of ant colony algorithm (ACA) such as stimulating factor, volatilisation coefficients and pheromone are all improved adaptively. Second, the fitness function values of particles' history optimal solutions after PSO coarse search are used to adjust the initial pheromone distribution in fine search of ACA. Finally, Baum-Welch algorithm (B-W) is adopted to locally modify the approximate global optimal solution. The new algorithm not only solves the BW dependency on initial values and the trapped local optimum problem, but also makes full use of the global search ability of IPSAA and local development ability of B-W. The experimental results show that the system using the new algorithm is more efficient, more stable, and has better classification performance than that of traditional HMM training algorithm.

A Novel Hybrid Evolution Optimization Algorithm and its Application

For the premature convergence and initial pheromone distribution problem of ant colony optimization algorithm, an improved particle swarm optimization (MPSO) algorithm is introduced into ant colony optimization algorithm in order to propose a novel hybrid evolution optimization (HEACO) algorithm in this paper. In the proposed HEACO algorithm, the ergodicity of the chaos is used to initialize the swarm in order to enhance the diversity of the particle swarm, and adjust the mutation probability and inertia weighting factor in order to improve the capability of local and global search. Then the MPSO algorithm is used to control the parameters of the heuristic factor, pheromone evaporation coefficient, and the stochastic selection threshold in order to effectively overcome the parameter influences of ACO, reduce the numbers of useless experiments and balance the developing optimal solution and enlarging search space. A series of typical traveling salesman problems are selected to validity the effectiveness of the proposed HEACO algorithm. The simulation results show that the performance of the proposed HEACO algorithm is better than the traditional ACO algorithm and PSO algorithm. So the proposed HEACO algorithm can effectively improve the solving efficiency and quality, and speed up the convergence and computation.