Cooperative Learning to Minimal Time Cost Attribute Reduction Through Ant Colony Optimization

2015 ◽  
Vol 12 (10) ◽  
pp. 4047-4057
Author(s):  
Ji Dong
Keyword(s):  
Cooperative Learning ◽  
Ant Colony Optimization ◽  
Attribute Reduction ◽  
Ant Colony ◽  
Time Cost ◽  
Minimal Time ◽  
Cost Attribute

scholarly journals Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zilong Xu ◽  
Hong Zhao ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Information Gain ◽  
Attribute Reduction ◽  
Ant Colony ◽  
Test Cost ◽  
Cost Attribute ◽  
Three Stages ◽  
Filtration Stage

Minimal test cost attribute reduction is an important problem in cost-sensitive learning. Recently, heuristic algorithms including the information gain-based algorithm and the genetic algorithm have been designed for this problem. However, in many cases these algorithms cannot find the optimal solution. In this paper, we develop an ant colony optimization algorithm to tackle this problem. The attribute set is represented as a graph with each vertex corresponding to an attribute and weight of each edge to pheromone. Our algorithm contains three stages, namely, the addition stage, the deletion stage, and the filtration stage. In the addition stage, each ant starts from the initial position and traverses edges probabilistically until the stopping criterion is satisfied. The pheromone of the traveled path is also updated in this process. In the deletion stage, each ant deletes redundant attributes. Two strategies, called the centralized deletion strategy and the distributed deletion strategy, are proposed. Finally, the ant with minimal test cost is selected to construct the reduct in the filtration stage. Experimental results on UCI datasets indicate that the algorithm is significantly better than the information gain-based one. It also outperforms the genetic algorithm on medium-sized dataset Mushroom.

scholarly journals Meta-Heuristic Algorithm based on Ant Colony Optimization Algorithm and Project Scheduling Problem (PSP) for the Traveling Salesman Problem

2021 ◽  
Vol 15 ◽  
pp. 68-77
Author(s):  
Alejandro Fuentes-Penna ◽  
Jorge A. Ruiz-Vanoye ◽  
Marcos S. González-Ramírez
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Time Cost ◽  
Ant Colony Optimization Algorithm ◽  
Main Target ◽  
Project Scheduling Problem ◽  
Time Distance ◽  
The Traveling Salesman Problem

The main target of Traveling Salesman Problem (TSP) is to construct the path with the lowest time between different cities, visiting every one once. The Scheduling Project Ant Colony Optimization (SPANCO) Algorithm proposes a way to solve TSP problems adding three aspects: time, cost effort and scope, where the scope is the number of cities, the effort is calculated multiplying time, distance and delivering weight factors and dividing by the sum of them and optimizing the best way to visit the cities graph.

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