traversal optimization is an iteration course of simulating ants leaving pheromones on paths in natural world.
Many research works dealing with the updating rules for [DELTA][[tau].sup.k.sub.ij] have been performed, like the Ant System (AS) , Elitist Ant System (EAS) , Rank-Based Ant System ([AS.sub.rank]) , Max-Min ant System (MMAS) , and Ant Colony
System (ACS) .
Keywords: ant colony
optimization, K-nearest neighbor, features selection, heuristic, pheromone.
Bio-inspired algorithms such as Ant Colony
Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Termite Hill (TH) are categorized as heuristic algorithms.
Purpose: The purpose of this research is to apply ant colony
optimization (ACO), a nature inspired computational (NIC) technique to achieve optimal feature subset selection, for the purpose of data dimensionality reduction, which will remove redundancy, provide a reduced storage space, improve memory utilization and subsequently, classification task.
For an ANT colony
was set to hold up the development of new hi-tech businesses in the city.
Optimization Based Load Balancing Algorithm :
algorithm (ACA) was first presented by Italian scholar Dorigo and Gambardella  to solve travelling salesman problem (TSP), and it has been applied to solve various kinds of combinatorial optimization problems afterwards [15-17].
Although these methods solved some problems, they still did not show significant effects with the large scale Genome-Wide Association Study datasets owing to the same "high-dimensional small sample size problem." With the rapid development of multiobjective optimization method and machine learning discipline, ant colony
optimization (ACO) algorithm was applied to epistasis research.
Then dynamic programming-tabu search algorithm and ant colony
one are used to solve and analyze different simulation examples so as to test the efficiency of solution.
For example, Genetic Algorithms and Ant Colony
Algorithms, as well as some other heuristic algorithms, are characterized by fast convergence and robustness.
The Ant Colony
Optimization Algorithms, are adaptive and use metaheuristic techniques (general purpose heuristic algorithms that can be applied to different problems with minor modifications), inspired in the emulation of the behavior of wild ant colonies, and in organization for finding food resources with the goal of survival of individuals .