Improved Ant Colony Algorithms for Eliminating Stagnation and Local Optimum Problem — A Survey

2017 ◽  
Author(s):  
A. Amuthan ◽  
K. Deepa Thilak
Keyword(s):  
Ant Colony ◽  
Local Optimum ◽  
Ant Colony Algorithms

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.

scholarly journals ANT COLONY OPTIMIZED ROUTING FOR MOBILE ADHOC NETWORKS (MANET)

2013 ◽  
pp. 218-223
Author(s):  
DWEEPNA GARG ◽  
PARTH GOHIL
Keyword(s):  
Swarm Intelligence ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Routing Algorithm ◽  
Ant Colony ◽  
Node Mobility ◽  
Mobile Nodes ◽  
Ant Colony Algorithms ◽  
Mobile Ad Hoc ◽  
Hoc Networks

A Mobile Ad-Hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without using centralized access points, infrastructure, or centralized administration. Routing means the act of moving information across an internet work from a source to a destination. The biggest challenge in this kind of networks is to find a path between the communication end points, what is aggravated through the node mobility. In this paper we present a new routing algorithm for mobile, multi-hop ad-hoc networks. The protocol is based on swarm intelligence. Ant colony algorithms are a subset of swarm intelligence and consider the ability of simple ants to solve complex problems by cooperation. The introduced routing protocol is well adaptive, efficient and scalable. The main goal in the design of the protocol is to reduce the overhead for routing. We refer to the protocol as the Ant Colony Optimization Routing (ACOR).

Ant Colony Algorithms: The State-of-the-Art

2006 ◽  
pp. 249-269
Author(s):  
Jihui Zhang ◽  
Junqin Xu ◽  
Siying Zhang
Keyword(s):  
State Of The Art ◽  
The State ◽  
Ant Colony ◽  
Ant Colony Algorithms

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.

Application of Ant Colony Algorithms in Path Planning of Ocean Survey

2019 ◽  
Vol 94 (sp1) ◽  
pp. 121
Author(s):  
Hongtao Yu ◽  
Sen Wang ◽  
Yan Bao
Keyword(s):  
Path Planning ◽  
Ant Colony ◽  
Ant Colony Algorithms
Sign in / Sign up

Export Citation Format

Share Document