Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control
A novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant C...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2018/1274969 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568147540967424 |
|---|---|
| author | Fevrier Valdez Juan Carlos Vazquez Fernando Gaxiola |
| author_facet | Fevrier Valdez Juan Carlos Vazquez Fernando Gaxiola |
| author_sort | Fevrier Valdez |
| collection | DOAJ |
| description | A novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant Colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem. |
| format | Article |
| id | doaj-art-67e85e308ec9404f9ea8a0ef3dfd4033 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-67e85e308ec9404f9ea8a0ef3dfd40332025-02-03T00:59:38ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2018-01-01201810.1155/2018/12749691274969Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature ControlFevrier Valdez0Juan Carlos Vazquez1Fernando Gaxiola2Tijuana Institute of Technology, Tijuana, BC, MexicoTijuana Institute of Technology, Tijuana, BC, MexicoAutonomous University of Chihuahua, Chihuahua, CHIH, MexicoA novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant Colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem.http://dx.doi.org/10.1155/2018/1274969 |
| spellingShingle | Fevrier Valdez Juan Carlos Vazquez Fernando Gaxiola Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control Advances in Fuzzy Systems |
| title | Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control |
| title_full | Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control |
| title_fullStr | Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control |
| title_full_unstemmed | Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control |
| title_short | Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control |
| title_sort | fuzzy dynamic parameter adaptation in aco and pso for designing fuzzy controllers the cases of water level and temperature control |
| url | http://dx.doi.org/10.1155/2018/1274969 |
| work_keys_str_mv | AT fevriervaldez fuzzydynamicparameteradaptationinacoandpsofordesigningfuzzycontrollersthecasesofwaterlevelandtemperaturecontrol AT juancarlosvazquez fuzzydynamicparameteradaptationinacoandpsofordesigningfuzzycontrollersthecasesofwaterlevelandtemperaturecontrol AT fernandogaxiola fuzzydynamicparameteradaptationinacoandpsofordesigningfuzzycontrollersthecasesofwaterlevelandtemperaturecontrol |