scholarly journals A Review: Study of various Ant Colony Optimization Techniques

IJARCCE ◽  
2015 ◽  
pp. 282-284
Author(s):  
Chandana A. Nighut ◽  
Samira F. Nigrel
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Review Study

Adapting Ant Colony for Topology Optimization of Compliant Mechanisms With Variable Material Density

2017 ◽  
Author(s):  
Nadim Diab
Keyword(s):  
Topology Optimization ◽  
Ant Colony Optimization ◽  
High Performance ◽  
Compliant Mechanisms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Intensity Level ◽  
Material Density ◽  
Swarm Intelligence Optimization

Swarm intelligence optimization techniques are widely used in topology optimization of compliant mechanisms. The Ant Colony Optimization has been implemented in various forms to account for material density distribution inside a design domain. In this paper, the Ant Colony Optimization technique is applied in a unique manner to make it feasible to optimize for the beam elements’ cross-section and material density simultaneously. The optimum material distribution algorithm is governed by two various techniques. The first technique treats the material density as an independent design variable while the second technique correlates the material density with the pheromone intensity level. Both algorithms are tested for a micro displacement amplifier and the resulting optimized topologies are benchmarked against reported literature. The proposed techniques culminated in high performance and effective designs that surpass those presented in previous work.

scholarly journals In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony optimization techniques

2010 ◽  
Vol 128 (11) ◽  
pp. 2673-2680 ◽  
Author(s):  
Mads Sylvest Bergholt ◽  
Wei Zheng ◽  
Kan Lin ◽  
Khek Yu Ho ◽  
Ming Teh ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Ant Colony Optimization ◽  
Optimization Techniques ◽  
Ant Colony ◽  
In Vivo Diagnosis

scholarly journals Optimization Techniques to Solve Travelling Salesman Problem Using Machine Learning Algorithms

2022 ◽  
Vol 10 (1) ◽  
pp. 274-279
Author(s):  
Prince Nathan S
Keyword(s):  
Machine Learning ◽  
Ant Colony Optimization ◽  
Travelling Salesman Problem ◽  
Learning Algorithms ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Ant Colony ◽  
Lasso Regression ◽  
Q Learning ◽  
The Right

Abstract: Travelling Salesmen problem is a very popular problem in the world of computer programming. It deals with the optimization of algorithms and an ever changing scenario as it gets more and more complex as the number of variables goes on increasing. The solutions which exist for this problem are optimal for a small and definite number of cases. One cannot take into consideration of the various factors which are included when this specific problem is tried to be solved for the real world where things change continuously. There is a need to adapt to these changes and find optimized solutions as the application goes on. The ability to adapt to any kind of data, whether static or ever-changing, understand and solve it is a quality that is shown by Machine Learning algorithms. As advances in Machine Learning take place, there has been quite a good amount of research for how to solve NP-hard problems using Machine Learning. This reportis a survey to understand what types of machine algorithms can be used to solve with TSP. Different types of approaches like Ant Colony Optimization and Q-learning are explored and compared. Ant Colony Optimization uses the concept of ants following pheromone levels which lets them know where the most amount of food is. This is widely used for TSP problems where the path is with the most pheromone is chosen. Q-Learning is supposed to use the concept of awarding an agent when taking the right action for a state it is in and compounding those specific rewards. This is very much based on the exploiting concept where the agent keeps on learning onits own to maximize its own reward. This can be used for TSP where an agentwill be rewarded for having a short path and will be rewarded more if the path chosen is the shortest. Keywords: LINEAR REGRESSION, LASSO REGRESSION, RIDGE REGRESSION, DECISION TREE REGRESSOR, MACHINE LEARNING, HYPERPARAMETER TUNING, DATA ANALYSIS

Gear Design Optimization Algorithms: A Review

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
YO Usman ◽  
PO Odion ◽  
EO Onibere ◽  
AY Egwoh
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Design Parameters ◽  
Gear Design ◽  
Complex Design

Gearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. Gears are round mechanical components with teeth arranged in their perimeter. Gear design is complex design that involves many design parameters and tables, finding an optimal or near optimal solution to this complex design is still a major challenge. Different optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing, Ant-Colony Optimization, and Neural Network etc., have been used for design optimization of the gear design problems. This paper focuses on the review of the optimization techniques used for gear design optimization with a view to identifying the best of them. Nowadays, the method used for the design optimization of gears is the evolutionary algorithm specifically the genetic algorithm which is based on the evolution idea of natural selection. The study revealed that GA. has the ability to find optimal solutions in a short time of computation by making a global search in a large search space. Keywords: Firefly Algorithm, Ant-Colony Optimization, Simulated Annealing, Genetic Algorithm, Gear design, Optimization, Particle Swarm Optimization Algorithm

Multi-view 3D scene reconstruction using ant colony optimization techniques

2012 ◽  
Vol 23 (11) ◽  
pp. 114002 ◽  
Author(s):  
Dimitrios Chrysostomou ◽  
Antonios Gasteratos ◽  
Lazaros Nalpantidis ◽  
Georgios C Sirakoulis
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Scene Reconstruction ◽  
3D Scene ◽  
3D Scene Reconstruction

scholarly journals A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 80 ◽  
Author(s):  
Avirup Guha Neogi ◽  
Singamreddy Mounika ◽  
Salagrama Kalyani ◽  
S A. Yogananda Sai
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Routing Problem ◽  
Time Window Constraints

Ant Colony Optimization (ACO) is a nature-inspired swarm intelligence technique and a metaheuristic approach which is inspired by the foraging behavior of the real ants, where ants release pheromones to find the best and shortest route from their nest to the food source. ACO is being applied to various optimization problems till date and has been giving good quality results in the field. One such popular problem is known as Vehicle Routing Problem(VRP). Among many variants of VRP, this paper presents a comprehensive survey on VRP with Time Window constraints(VRPTW). The survey is presented in a chronological order discussing which of the variants of ACO is used in each paper followed by the advantages and limitations of the same.  

Sign in / Sign up

Export Citation Format

Share Document