|
Utilizing the Unified Ant Colony Algorithm by Chaotic Maps
|
Hamid Reza Yousefzadeh *    , Davood Darvishi , Arezoo Sayadi Salar |
| Payame Noor University , usefzadeh.math@pnu.ac.ir |
|
|
Abstract: (5921 Views) |
Ant colony optimization (ACOR) is a meta-heuristic algorithm for solving continuous optimization
problems (MOPs). In the last decades, some improved versions of ACOR have been proposed.
The UACOR is a unified version of ACOR that is designed for continuous domains. By adjusting
some specified components of the UACOR, some new versions of ACOR can be deduced. By doing
that, it becomes more practical for different types of MOPs. Based on the nature of meta-heuristic
algorithms, the performance of meta-heuristic algorithms are depends on the exploitation and
exploration, which are known as the two useful factors to generate solutions with different
qualities. Since all the meta-heuristic algorithms with random parameters use the probability
functions to generate the random numbers and as a result, there is no any control over the
amount of diversity; hence in this paper, by using the best parameters of UACOR and making
some other changes, we propose a new version of ACOR to increase the efficiency of UACOR.
These changes include using chaotic sequences to generate various random sequences and also
using a new local search to increase the quality of the solution. The proposed algorithm, the two
standard versions of UACOR and the genetic algorithm are tested on the CEC05 benchmark
functions, and then numerical results are reported. Furthermore, we apply these four algorithms
to solve the utilization of complex multi-reservoir systems, the three-reservoir system of Karkheh
dam, as a case study. The numerical results confirm the superiority of proposed algorithm over
the three other algorithms.
|
|
| Keywords: Ant colony algorithm, Continuous optimization, Chaotic sequences, Multi-reservoir systems, Genetic algorithm. |
|
|
Full-Text [PDF 461 kb]
(8323 Downloads)
|
Type of Study: Original |
Subject:
Mathematical Modeling and Applications of OR
Received: 2021/04/11 | Accepted: 2020/09/20 | Published: 2020/09/20
|
|
|
|
|
|
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
