Efficient Ant Colony Optimization (EACO) Algorithm for Deterministic OptimizationUrmila M Diwekar*and Berhane H Gebreslassie
Center for Uncertain Systems: Tools for Optimization & Management (CUSTOM), Vishwamitra Research Institute, Crystal Lake, USA
- Corresponding Author:
- Urmila M Diwekar
Center for Uncertain Systems: Tools for Optimization & Management (CUSTOM)
Vishwamitra Research Institute, Crystal Lake, USA
Email: [email protected]
Received date: March 03, 2016 Accepted date: March 06, 2016 Published date: March 10, 2016
Citation: Diwekar UM, Gebreslassie BH (2016) Efficient Ant Colony Optimization (EACO) Algorithm for Deterministic Optimization. Int J Swarm Intel Evol Comput 5:131. doi:10.4172/2090-4908.1000131
Copyright: © 2016 Diwekar UM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In this paper, an efficient ant colony optimization (EACO) algorithm is proposed based on efficient sampling method for solving combinatorial, continuous and mixed-variable optimization problems. In EACO algorithm, Hammersley Sequence Sampling (HSS) is introduced to initialize the solution archive and to generate multidimensional random numbers. The capabilities of the proposed algorithm are illustrated through 9 benchmark problems. The results of the benchmark problems from EACO algorithm and the conventional ACO algorithm are compared. More than 99% of the results from the EACO show efficiency improvement and the computational efficiency improvement range from 3% to 71%. Thus, this new algorithm can be a useful tool for large-scale and wide range of optimization problems. Moreover, the performance of the EACO is also tested using the five variants of ant algorithms for combinatorial problems.