Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers
AbstractThis article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem using different performance metrics depending on the problem. The algorithms used in the preliminary tests are: the firefly algorithm (FA), which is inspired by blinking fireflies; wind-driven optimization (WDO), which is inspired by the movement of the wind in the atmosphere, and in which the positions and velocities of the wind packages are updated; and finally, drone squadron optimization (DSO)—the inspiration for this method is new and interesting—based on artifacts, where drones have a command center that sends information to individual drones and updates their software to optimize the objective function. The proposed model helps discover the best method to solve a specific problem, and also reduces the time that it takes to search for methods before finding the one that obtains the most satisfactory results. The main idea is that with this competitiveness approach, methods are tested at the same time until the best one to solve the problem in question is found. As preliminary tests of the model, the optimization of the benchmark mathematical functions and membership functions of a fuzzy controller of an autonomous mobile robot was used. View Full-Text
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Lagunes, M.L.; Castillo, O.; Valdez, F.; Soria, J. Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers. Algorithms 2019, 12, 90.
Lagunes ML, Castillo O, Valdez F, Soria J. Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers. Algorithms. 2019; 12(5):90.Chicago/Turabian Style
Lagunes, Marylu L.; Castillo, Oscar; Valdez, Fevrier; Soria, Jose. 2019. "Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers." Algorithms 12, no. 5: 90.
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