An optimization algorithm based on ant colony algorithm

2012 ◽  
Author(s):  
Kuang Xiangling ◽  
Huang Guangqiu
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

2021 ◽  
Author(s):  
Yueping Chen ◽  
Naiqi Shang
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Path Optimization ◽  
Inspection Time ◽  
Free Form ◽  
Coordinate Measuring Machines ◽  
Ant Colony Optimization Algorithm ◽  
Measuring Machine ◽  
Free Form Surfaces

Abstract Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

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.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

Study of Sintering Blending Based on Swarm Intelligence Optimization Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1550-1553 ◽  
Author(s):  
Hui Zhao ◽  
Ming Wang ◽  
Hong Jun Wang ◽  
You Jun Yue
Keyword(s):  
Exact Solution ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Positive Feedback Mechanism ◽  
Swarm Intelligence Optimization

The sintering blending is a complex nonlinear optimization problem. The traditional single algorithm can not meet the requirement of good quality of sinter and lowest costs well. So, a hybrid optimization method of particle swarm and ant colony algorithm was proposed. The method gives full play to the global convergence of particle swarm optimization algorithm, takes it as a preliminary search, then use the positive feedback mechanism of ant colony algorithm for the exact solution, to make these two algorithms to reach a complementary, in order to get a rapid exact solution. The simulation results show that the proposed hybrid algorithm has fast convergence and high accuracy, which can effectively reduce the sintering cost.

scholarly journals Intelligent Bionic Optimization Algorithm Based on the Growth Characteristics of Tree Branches

2021 ◽  
Vol 15 (2) ◽  
pp. 34-46
Author(s):  
Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Morphological Characteristics ◽  
Optimal Solution ◽  
Shoot Tip ◽  
Experimental Results ◽  
Ant Colony ◽  
The Individual

To improve the performance of bionic algorithms, an intelligent bionic optimization algorithm is proposed based on the morphological characteristics of trees growing toward light. The growth organ of the tree is mapped into the coding of the tree growth algorithm, and the entire tree is formed by selecting the fastest growing individual to form the next level of the tree. When the tree growth reaches a certain level, the individual code of the shoot tip is added to enhance the search ability of the individual shoot tip in the growth space of the entire tree. This method achieves a near-optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function. The experimental results show that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm or the ant colony algorithm.

scholarly journals Low-Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xueli Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Ant Colony Optimization Algorithm

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

Research on Path Optimization of Automated Warehouse Based on Ant Colony Algorithm

2011 ◽  
Vol 201-203 ◽  
pp. 1112-1115
Author(s):  
Hao Ping Li ◽  
Zi Fan Fang ◽  
Ying Wang
Keyword(s):  
Mathematical Model ◽  
Energy Consumption ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Operation Time ◽  
Optimization Method ◽  
Ant Colony ◽  
Path Optimization ◽  
Operating Costs ◽  
Reduce Energy Consumption

Based on analysis of the cargo selecting strategy of fixed shelf automated warehouse, the idea of path optimization is put forward and the stacker path optimization method is studied. A mathematical model of stacker operation path optimization is built to minimize the length of operation path and the operation time. The model is solved by using the ant colony optimization method. Simulation shows that chosen stacker operation path by using the optimization model and optimization algorithm, can not only reduce energy consumption and warehouse operating costs, but also improve the efficiency of goods storage.

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.

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