Evaluations of Ant Colony Optimization Inspired SCTP Optimal Path Selection Using E-model

2012 ◽  
Author(s):  
Muhammad Ariff Baharudin ◽  
Quang Tran Minh ◽  
Eiji Kamioka
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Path ◽  
Path Selection ◽  
Ant Colony

scholarly journals Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

2020 ◽  
Vol 17 (3) ◽  
pp. 165-173
Author(s):  
C.O. Yinka-Banjo ◽  
U. Agwogie
Keyword(s):  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Fruit Fly ◽  
Convergence Speed ◽  
Ant Colony ◽  
Computational Time ◽  
Robot Path Planning ◽  
Nature Inspired Algorithms ◽  
Robot Path

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords:  Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

Mobile Robot Motion Planning to Avoid Obstacle Using Modified Ant Colony Optimization

2015 ◽  
Vol 776 ◽  
pp. 396-402 ◽  
Author(s):  
Nukman Habib ◽  
Adi Soeprijanto ◽  
Djoko Purwanto ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Initial Position ◽  
Ant Colony ◽  
Robot Motion ◽  
Complex Environments ◽  
Point To Point ◽  
Global Planning

The ability of mobile robot to move about the environment from initial position to the goal position, without colliding the obstacles is needed. This paper presents about motion planning of mobile robot (MR) in obstacles-filled workspace using the modified Ant Colony Optimization (M-ACO) algorithm combined with the point to point (PTP) motion in achieving the static goal. Initially, MR try to plan the path to reach a goal, but since there are obstacles on the path will be passed through so nodes must be placed around the obstacles. Then MR do PTP motion through this nodes chosen by M-ACO, in order to form optimal path from the choice nodes until the last node that is free from obstacles. The proposed approach shows that MR can not only avoid collision with obstacle but also make a global planning path. The simulation result have shown that the proposed algorithm is suitable for MR motion planning in the complex environments with less running time.

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.

scholarly journals Recent research in finding the optimal path by ant colony optimization

2021 ◽  
Vol 10 (2) ◽  
pp. 1015-1023
Author(s):  
Sari Ali Sari ◽  
Kamaruddin Malik Mohamad
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Path ◽  
Classification Problem ◽  
Ant Colony ◽  
Future Research ◽  
Research Directions ◽  
File Carving ◽  
Critical Problems ◽  
Future Research Directions ◽  
Recent Trends

The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.

scholarly journals Self-Adjusting Ant Colony Optimization Based on Information Entropy for Detecting Epistatic Interactions

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 114 ◽  
Author(s):  
Boxin Guan ◽  
Yuhai Zhao
Keyword(s):  
Ant Colony Optimization ◽  
Information Entropy ◽  
Large Scale ◽  
Association Studies ◽  
Detection Algorithm ◽  
Path Selection ◽  
Ant Colony ◽  
Selection Strategy ◽  
Nucleotide Polymorphisms ◽  
Epistatic Interactions

The epistatic interactions of single nucleotide polymorphisms (SNPs) are considered to be an important factor in determining the susceptibility of individuals to complex diseases. Although many methods have been proposed to detect such interactions, the development of detection algorithm is still ongoing due to the computational burden in large-scale association studies. In this paper, to deal with the intensive computing problem of detecting epistatic interactions in large-scale datasets, a self-adjusting ant colony optimization based on information entropy (IEACO) is proposed. The algorithm can automatically self-adjust the path selection strategy according to the real-time information entropy. The performance of IEACO is compared with that of ant colony optimization (ACO), AntEpiSeeker, AntMiner, and epiACO on a set of simulated datasets and a real genome-wide dataset. The results of extensive experiments show that the proposed method is superior to the other methods.

Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique

2020 ◽  
Vol 47 (4) ◽  
pp. 535-545
Author(s):  
Saroj Kumar ◽  
Dayal R. Parhi ◽  
Manoj Kumar Muni ◽  
Krishna Kant Pandey
Keyword(s):  
Mobile Robots ◽  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Optimal Path ◽  
Ant Colony ◽  
Target Point ◽  
Multiple Mobile Robots ◽  
Content Type ◽  
Path Search ◽  
Sine Cosine Algorithm

Purpose This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments. Design/methodology/approach The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point. Findings Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these. Originality/value Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.

scholarly journals Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 15459-15471 ◽  
Author(s):  
Xiangyuan Jiang ◽  
Zongyuan Lin ◽  
Tianhao He ◽  
Xiaojing Ma ◽  
Sile Ma ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Search Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Path Finding
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