Research on BP Neural Network Optimization Based on Ant Colony Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1819-1821 ◽  
Author(s):  
Rui Wang ◽  
Na Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Initial Value ◽  
Neural Network Optimization ◽  
Optimal Weights

This paper presents an ant colony algorithm and BP algorithm together to complete the learning algorithm for neural networks ACO-BP algorithm. The algorithm adopts the ant colony algorithm for global optimization of the network weights, overcome the disadvantage of BP algorithm that is easy to fall into local optimum; then, the optimal weights found by BP algorithm as the initial value, further optimization. Finally, the simulation experiments show that, if the network structure is determined by the condition, this algorithm not only speeds up the convergence speed of the improved ant colony algorithm of optimal solution, but also can avoid falling into local optimal path. It will increase the reliability.

Cutting trajectory planning of sections with complex composition for roadheader

2018 ◽  
Vol 233 (4) ◽  
pp. 1441-1452 ◽  
Author(s):  
Suyu Wang ◽  
Miao Wu
Keyword(s):  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Complex Composition ◽  
Cutting Head ◽  
Pheromone Concentration ◽  
Simulation Results

In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

A Improved Pareto of Ant Colony Algorithm to Solve the Vehicle Routing Problem with Time Windows

2014 ◽  
Vol 1030-1032 ◽  
pp. 1941-1944
Author(s):  
Hong Dou Zhang ◽  
Ning Guo ◽  
Jian Lin Mao ◽  
Hai Feng Wang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Time Windows ◽  
Optimal Solution ◽  
Ant Colony ◽  
Local Optimum ◽  
Routing Problem ◽  
Important Extension

Vehicle routing problem with time Windows (VRPTW) that is a kind of important extension type for VPR. In view of problem which the ant colony algorithm in solving VRPTW easily plunged into local optimum , this paper defines a new ant transition probability of saving ideas, and uses the Pareto optimal solution set of global pheromone updating rule, and puts forward a kind of improved Pareto ant colony algorithm (IPACA) . Through the simulation experiments show that IPACA improves the global search ability of ACA, effectively avoids the algorithm falls into local optimum, and reduces the total distribution cost (distance), so as to verify the effectiveness of the proposed algorithm.

scholarly journals AGV optimal path planning based on improved ant colony algorithm

2018 ◽  
Vol 232 ◽  
pp. 03052 ◽  
Author(s):  
Chengwei He ◽  
Jian Mao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Undirected Network ◽  
Optimal Path Planning ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Improved Ant Colony Algorithm

Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.

scholarly journals An Improved Ant Colony Algorithm and Its Application in Vehicle Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Min Huang ◽  
Ping Ding
Keyword(s):  
Path Planning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Optimal Path ◽  
Optimal Solution ◽  
Planning Method ◽  
Ant Colony ◽  
Routing Problem

Optimal path planning is an important issue in vehicle routing problem. This paper proposes a new vehicle routing path planning method which adds path weight matrix and save matrix. The method uses a new transition probability function adding the angle factor function and visibility function, while setting penalty function in a new pheromone updating model to improve the accuracy of the route searching. Finally, after each cycle, we use 3-opt method to update the optimal solution to optimize the path length. The results of comparison also confirm that this method is better than the traditional ant colony algorithm for vehicle routing path planning method. The result of computer simulation confirms that the method can plan a more rational rescue path focused on the real traffic situation.

scholarly journals Research on path planning of cleaning robot based on an improved ant colony algorithm

2021 ◽  
Vol 336 ◽  
pp. 07005
Author(s):  
Zhidong Wang ◽  
Changhong Wu ◽  
Jing Xu ◽  
Hongjie Ling
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Cleaning Robot ◽  
Initial Stage ◽  
Searching Ability ◽  
Improved Ant Colony Algorithm

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.

Optimal Path Selection for Mine Emergency Rescue Based on Improved Ant Colony Algorithm

2010 ◽  
Vol 159 ◽  
pp. 168-171
Author(s):  
Feng Tian ◽  
Zhong Zhao Chen
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Lower Solution ◽  
Path Selection ◽  
Ant Colony ◽  
Emergency Rescue ◽  
Local Optimal Solution ◽  
Solution Accuracy ◽  
Improved Ant Colony Algorithm

It is the primary task to ensure the safety of lives and property of underground workers with the increasing amount of coal mining. Under the actual complex environment of coal, all kinds of uncertian factors should be considered except for the distance for the selection of the optimal path to reduce casualties. Aiming at the defect of the lower solution accuracy and tending to fall into local optimal solution of the basic ant colony algorithm(ACA), an improved ant colony algorithm is presented based on the model of ACA. Experiment results show that the new algorithm can get better results.

Research of In-Car Navigation Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 461-465
Author(s):  
Fang Ding ◽  
Su Zhuo Wu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony System ◽  
Navigation Systems ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm ◽  
Car Navigation

Determining how to select path efficiently in complex transportation networks was one of the main problems in-car navigation systems. For the drawbacks of slow convergence and easy to fall into local optimal solution of basic ant colony algorithm in solving the optimal path problem, a method of improving the expect-heuristic function is proposed in this paper, which enhances search direction and improves the convergence rate. Meanwhile, with the introduction of a new strategy to update the pheromone on ant colony system, the contradiction that convergence speed brings stagnation is balanced. The results show that the improved ant colony algorithm is easier to get the optimal solution compared with basic ant colony algorithm, and the convergence speed is faster, having a good navigation effect.

scholarly journals Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weichuan Ni ◽  
Zhiming Xu ◽  
Jiajun Zou ◽  
Zhiping Wan ◽  
Xiaolei Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Performance ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Routing Optimization ◽  
Ipv6 Network

The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user’s needs for network service quality, network performance, and other aspects.

Study on Traveling Salesman Problem Based on the Improved Chaos Ant Colony Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2196-2199 ◽  
Author(s):  
Hai Yang
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Elite Strategy ◽  
Novel Method ◽  
Simulation Results ◽  
Improved Algorithm

In this paper an improved chaos ant colony algorithm based on return optimization strategy, elite strategy and intersection removal strategy is proposed. The improved algorithm uses orthogonal method to cluster the target points, then adopt chaos technology to optimize initial solution of the ant colony to improve individual quality and chaos perturbation is utilized to avoid the search being trapped into local optimum solutions. The simulation results show that the improved algorithm has higher efficiency in finding optimal path and it is a novel method to solve traveling salesmen problem.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

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.

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