A Novel Collaborative Optimization Algorithm for Solving TSP

2014 ◽  
Vol 543-547 ◽  
pp. 1795-1798
Author(s):  
Di Zhou
Keyword(s):  
Ant Colony Algorithm ◽  
Pso Algorithm ◽  
Collaborative Optimization ◽  
Ant Colony ◽  
Optimization Strategy ◽  
Distribution Problem ◽  
Chaos Optimization ◽  
Exchange Operation ◽  
Optimal Values ◽  
Simulation Results

For the premature convergence and initial pheromone distribution problem of the basic ACO algorithm, PSO algorithm and chaos optimizing strategy are introduced into the ant colony algorithm in order to propose a novel collaborative optimization (CPACO) algorithm based on the collaboration theory. The first, the CPACO algorithm divides the ant colony into several subgroups, and the parameters of the subgroup are regarded as the particles. Then these advantages of PSO algorithm and chaos optimization strategy are fully utilized to optimize these parameters of the ACO algorithm in order to obtain the optimal values of these parameters. And the pheromone exchange operation is introduced into the subgroup. In order to validate the performance of the CPACO algorithm, the TSP problems are selected in here. The simulation results show that the proposed CPACO algorithm has better optimization performance than the traditional ACO algorithm.

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 Research on the Uncertainty Decision Model of the Regional Collaborative Innovation System Based on an Improved Ant Colony Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Xiaona Zhang ◽  
Fayin Wang
Keyword(s):  
Decision Model ◽  
Ant Colony Algorithm ◽  
Innovation System ◽  
Path Selection ◽  
Collaborative Optimization ◽  
Ant Colony ◽  
Evaluation Index System ◽  
Regional Innovation ◽  
Collaborative Innovation ◽  
Improved Ant Colony Algorithm

The regional collaborative innovation system is a nonlinear complex system, which has obvious uncertainty characteristics in the aspects of member selection and evolution. Ant colony algorithm, which can do the uncertainty collaborative optimization decision-making, is an effective tool to solve the uncertainty decision path selection problem. It can improve the cooperation efficiency of each subsystem and achieve the goal of effective cooperation. By analysing the collaborative evolution mechanisms of the regional innovation system, an evaluation index system for the regional collaborative innovation system is established considering the uncertainty of collaborative systems. The collaborative uncertainty decision model is constructed to determine the regional innovation system’s collaborative innovation effectiveness. The improved ant colony algorithm with the pheromone evaporation model is applied to traversal optimization to identify the optimal solution of the regional collaborative innovation system. The collaboration capabilities of the ant colony include pheromone diffusion so that local updates are more flexible and the result is more rational. Finally, the method is applied to the regional collaborative innovation system.

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.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

Ant Colony Algorithm Application In Glass Fiber Textile Machine Parameter Tuning

2012 ◽  
Vol 462 ◽  
pp. 71-76 ◽  
Author(s):  
Li Hong Zhang ◽  
Shu Qian Chen ◽  
Gui Zhi Bai
Keyword(s):  
Glass Fiber ◽  
Pid Control ◽  
Ant Colony Algorithm ◽  
Control Process ◽  
Parameter Tuning ◽  
Integral Function ◽  
Parameters Optimization ◽  
Ant Colony ◽  
Control Parameters ◽  
Optimization Strategy

In glass fiber textile process, non-axis volume cloth drive motor with glass fabric volume increases, increasing the pressure on the drive shaft, moreover, because of cloth non-axis volume makes the pressure in the process of change is evident, that causes the motor load changing constantly, the traditional PID control system controller cannot timely tracking response. In order to solve the problem which the control parameters optimizes, improves the system performance, proposed a new Ant colony algorithm PID parameters optimization strategy, this solution can combine characteristics that Ant colony algorithm can fast find the most superior parameter solution stably and PID can precise adjustment. In the control process, taken the PID parameters as a colony of ants, used to control the absolute error integral function as the optimization objective, dynamically adjust the PID control parameters in the control process, so as to realize the PID parameters on-line tuning.

Path Planning of Robot Based on Ant Colony Optimization Algorithm

2014 ◽  
Vol 614 ◽  
pp. 199-202 ◽  
Author(s):  
Bao Ming Shan ◽  
De Xiang Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Robot Path Planning ◽  
Ant Colony Optimization Algorithm ◽  
Simulation Results ◽  
Robot Path

This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.

Application of Improved Ant Colony Algorithm in QoS Routing Optimization

2010 ◽  
Vol 108-111 ◽  
pp. 353-358 ◽  
Author(s):  
Xiu Ju Liu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Qos Routing ◽  
Ant Colony ◽  
Design And Implementation ◽  
Routing Optimization ◽  
Speed Up ◽  
Simulation Results ◽  
Full Proof ◽  
Implementation Steps

QoS routing for the characteristics of the ant colony algorithm is improved. First of all defect from the ant colony algorithm and to increase Network Node bound, As well as to speed up global convergence analysis of the three areas of the ant colony algorithm to improve thinking; And by improving the ant colony algorithm in the QoS routing optimization application, the details of the ant colony algorithm to improve the design and implementation steps. Finally, simulation results show that: According to the algorithm for solving the problem and get the optimal solution of the ratio of full proof of improvement in the ant colony algorithm QoS routing optimization on the effectiveness and stability.

scholarly journals ACTIVE POWER LOSS REDUCTION BY AMPLIFIED ANT COLONY ALGORITHM

2018 ◽  
Vol 6 (7) ◽  
pp. 132-141
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Active Power ◽  
Ant Colony ◽  
Superior Performance ◽  
Loss Reduction ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

In this paper, Amplified Ant Colony (AAC) algorithm has been proposed for solving optimal reactive power problem. Mutation of Genetic algorithm (GA) is used in Ant Colony Algorithm (ACA) and the output of the GA is given as an input to the ACA. The proposed Amplified Ant Colony (AAC) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the proposed Amplified Ant Colony (AAC) algorithm in reducing the real power loss & voltage profiles are within the limits.

Research on 3D Route Planning for UAV in Low-Altitude Penetration Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 442 ◽  
pp. 556-561 ◽  
Author(s):  
Tao Deng ◽  
Zi Ming Xiong ◽  
Yong Jun Liu ◽  
Qing Zhi Meng
Keyword(s):  
Ant Colony Algorithm ◽  
Route Planning ◽  
Ant Colony ◽  
Ant Algorithm ◽  
Ant Colonies ◽  
Simulation Results ◽  
Low Altitude ◽  
Route Cost ◽  
Yaw Angle ◽  
Improved Ant Colony Algorithm

It has discussed the approach to establish the models of terrain, threat and the evaluation of route cost in the route planning for UAV in low-altitude penetration. An Ant Algorithm is introduced into UAV route planning, since stagnation may appear during searching in use of traditional ant colonies algorithm,this paper introduces yaw angle to improve heuristic information, establishes the prior search set, makes ant colony algorithm the more rapid and effective search to the best route, simulation results prove the efficiency of the planning method.

A Dynamic Edge Exchanged Ant Colony Algorithm for TSP Problem

2014 ◽  
Vol 687-691 ◽  
pp. 1608-1611
Author(s):  
Yu Zhong Liu ◽  
Hua Ping Yu
Keyword(s):  
Ant Colony Algorithm ◽  
Heuristic Algorithms ◽  
Local Optimization ◽  
Ant Colony ◽  
Premature Convergence ◽  
Local Optimum ◽  
Dynamic Adjustment ◽  
Low Speed ◽  
Simulation Results ◽  
Dynamic Exchange

Aimed at solving premature convergence and low speed in heuristic algorithms for TSP problems, this paper analyzed the principle of Max-Min Ant colony algorithm (MMAS) and Lin-Kernighan algorithm, then proposed a dynamic exchange of Max-Min Ant colony algorithm (MMAS-LK). The new algorithm used MMAS to initially a set of the solutions in the early state, then utilized the improved Lin-Kernighan algorithm for local optimization, and dynamic adjustment parameters according to the process of computing avoid falling into local optimum. The simulation results showed that the proposed algorithm compared with the original MMAS and Lin-Kernighan algorithm, it has a better speed and precision in the TSP problem.

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