A Method for the Team Selection Problem Between Two Decision-Makers Using the Ant Colony Optimization

2017 ◽  
pp. 391-410 ◽  
Author(s):  
Marilyn Bello ◽  
Rafael Bello ◽  
Ann Nowé ◽  
María M. García-Lorenzo
Keyword(s):  
Ant Colony Optimization ◽  
Decision Makers ◽  
Ant Colony ◽  
Selection Problem ◽  
Team Selection

Ant colony optimization for the real-time train routing selection problem

2016 ◽  
Vol 85 ◽  
pp. 89-108 ◽  
Author(s):  
Marcella Samà ◽  
Paola Pellegrini ◽  
Andrea D’Ariano ◽  
Joaquin Rodriguez ◽  
Dario Pacciarelli
Keyword(s):  
Ant Colony Optimization ◽  
Real Time ◽  
Ant Colony ◽  
Selection Problem ◽  
The Real ◽  
Train Routing

scholarly journals Train routing selection problem: Ant colony optimization versus integer linear programming

2021 ◽  
Vol 54 (2) ◽  
pp. 167-172
Author(s):  
Bianca Pascariu ◽  
Marcella Samà ◽  
Paola Pellegrini ◽  
Andrea D’Ariano ◽  
Dario Pacciarelli ◽  
...  
Keyword(s):  
Linear Programming ◽  
Ant Colony Optimization ◽  
Integer Linear Programming ◽  
Ant Colony ◽  
Selection Problem ◽  
Train Routing

Multiple parameter control for ant colony optimization applied to feature selection problem

2015 ◽  
Vol 26 (7) ◽  
pp. 1693-1708 ◽  
Author(s):  
Gang Wang ◽  
HaiCheng Eric Chu ◽  
Yuxuan Zhang ◽  
Huiling Chen ◽  
Weitong Hu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Selection Problem ◽  
Parameter Control ◽  
Feature Selection Problem ◽  
Multiple Parameter

Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

2010 ◽  
Author(s):  
Gour Sundar Mitra Thakur ◽  
Rupak Bhattacharyya ◽  
Swapan Kumar Mitra ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Portfolio Selection ◽  
Ant Colony ◽  
Selection Problem ◽  
Optimization Approach ◽  
Portfolio Selection Problem ◽  
Fuzzy Random

scholarly journals A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
FuTao Zhao ◽  
Zhong Yao ◽  
Jing Luan ◽  
Xin Song
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Supplier Selection ◽  
Optimal Solution ◽  
Ant Colony ◽  
Selection Problem ◽  
The Novel ◽  
Effective Feedback ◽  
Innovative Solution ◽  
Supplier Selection Problem

A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.

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