Evolutionary Approach to the Optimal Design of Fuzzy Controllers for Trajectory Tracking

Mancilla, Alejandra; Castillo, Oscar; García-Valdéz, Mario

doi:10.1007/978-3-030-85626-7_54

Cited by 4 publications

(1 citation statement)

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“…In previous work [14], we established that tuning the MFs of fuzzy controllers using population-based metaheuristics demands the extensive use of computational resources. This demand stems from establishing the fitness of all candidate solutions, which requires running several simulations [15,16] for each candidate; this is a problem inherent to population-based algorithms. However, because evolutionary algorithms evaluate candidate solutions in isolation; they are a perfect match for their parallel execution.…”

Section: Introductionmentioning

confidence: 99%

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

et al. 2023

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Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.

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Section: Introductionmentioning

confidence: 99%