Solenopsis: A Framework for the Development of Ant Algorithms

2007 ◽  
Author(s):  
Amos Brocco ◽  
Beat Hirsbrunner ◽  
Michele Courant
Keyword(s):  
Ant Algorithms

scholarly journals Application of cellular automatons and ant algorithms in avionics

2018 ◽  
Vol 973 ◽  
pp. 012062
Author(s):  
A V Kuznetsov ◽  
N I Selvesiuk ◽  
G A Platoshin ◽  
E V Semenova
Keyword(s):  
Ant Algorithms ◽  
Cellular Automatons

Applicability of Genetic and Ant Algorithms in Highway Alignment and Rail Transit Station Location Optimization

2012 ◽  
Vol 3 (1) ◽  
pp. 13-36 ◽  
Author(s):  
Sutapa Samanta ◽  
Manoj K. Jha
Keyword(s):  
Optimization Problems ◽  
Ant Algorithm ◽  
Rail Transit ◽  
Search Problems ◽  
Location Optimization ◽  
Ant Algorithms ◽  
Transit Station ◽  
Similar Solutions ◽  
Station Location ◽  
Highway Alignment

The emergence of artificial intelligence (AI)-based optimization heuristics like genetic and ant algorithms is useful in solving many complex transportation location optimization problems. The suitability of such algorithms depends on the nature of the problem to be solved. This study examines the suitability of genetic and ant algorithms in two distinct and complex transportation problems: (1) highway alignment optimization and (2) rail transit station location optimization. A comparative study of the two algorithms is presented in terms of the quality of results. In addition, Ant algorithms (AAs) have been modified to search in a global space for both problems, a significant departure from traditional AA application in local search problems. It is observed that for the two optimization problems both algorithms give almost similar solutions. However, the ant algorithm has the inherent limitation of being effective only in discrete search problems. When applied to continuous search spaces ant algorithm requires the space to be sufficiently discretized. On the other hand, genetic algorithms can be applied to both discrete and continues spaces with reasonable confidence. The application of AA in global search seems promising and opens up the possibility of its application in other complex optimization problems.

scholarly journals On the Resilience of Ant Algorithms. Experiment with Adapted MMAS on TSP

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 752
Author(s):  
Elena Nechita ◽  
Gloria Cerasela Crişan ◽  
Laszlo Barna Iantovics ◽  
Yitong Huang
Keyword(s):  
Real Life ◽  
Future Research ◽  
Heuristic Methods ◽  
Ant System ◽  
Research Directions ◽  
Ant Algorithms ◽  
Software Applications ◽  
Communication Processes ◽  
Future Research Directions ◽  
Broad Outline

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX–MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems.

Coevolutionary Ant Algorithms Playing Games

2002 ◽  
pp. 298-299
Author(s):  
Jürgen Branke ◽  
Michael Decker ◽  
Daniel Merkle ◽  
Hartmut Schmeck
Keyword(s):  
Ant Algorithms

Based on Ant Colony Algorithm Simulation of Single Crystal Alloy Finite Element

2012 ◽  
Vol 201-202 ◽  
pp. 549-552
Author(s):  
Shu Zhang ◽  
Lei Meng
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Ant Colony Algorithm ◽  
Fem Analysis ◽  
Control Parameters ◽  
Algorithm Optimization ◽  
Ant System ◽  
Ant Colonies ◽  
Ant Algorithms ◽  
System A

we have used the metaphor of ant colonies to define "the Ant system", a class of distributed algorithms for combinatorial optimization. In this paper we analyze some properties of Ant-cycle, the up to now best performing of the ant algorithms we have tested. We report many results regarding its performance when varying the values of control parameters and we compare it with some FEM algorithms. And in accordance with treatment principles, the microstructure of the alloy is simulated. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

Sign in / Sign up

Export Citation Format

Share Document