Improving the Energy Efficiency of Evolutionary Multi-objective Algorithms

Moreno, Juan José Hurtado; Ortega, Gloria; Filatovas, Ernestas; Martínez, J.A.

doi:10.1007/978-3-319-49956-7_5

Cited by 1 publication

(1 citation statement)

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“…Hence, they are focused on other types or aspects of problems involving UAVs, while this work is focused on presenting a distributable framework for target search missions. Finally, and as our DEVS-based framework also implements the optimization of the UAV and sensor trajectories, we want to mention a few works that have successfully used DEVS to optimize problems of other fields of research (Cárdenas et al, 2020;Moreno, de la Torre, Risco-Martin, Besada-Portas, Aranda, & Ayala, 2011;Ntaimo, Hu, & Sun, 2008;Pérez, 2017;Risco, Mittal, Atienza, Hidalgo, & Lanchares, 2008;Zeigler, Moon, Lopes, & Kim, 1996). This last group of works already shows how implementing the optimizer within DEVS is helpful to unify the modeling and implementation under the same paradigm.…”

Section: Introductionmentioning

confidence: 99%

Cloud DEVS-based computation of UAVs trajectories for search and rescue missions

Bordón-Ruiz

Besada-Portas

López-Orozco

2022

Journal of Simulation

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This paper presents a new Cloud-deployable DEVS-based framework for optimizing UAV trajectories and sensor strategies in target-search missions. Its DEVS-support provides the framework with a well-established, flexible and verifiable modeling strategy to include different types of models for the UAV, sensor and target dynamics; for the target and sensor uncertainty, and for the optimizing process. Its Cloud deployability allows speeding up the hundreds of evaluations/simulations required to optimize this NP-hard problem which involves computationally-heavy models when solving real-world missions. The framework, carefully designed to handle different types of target-search missions, currently optimizes, using a multi-objective Genetic Algorithm, free-shape trajectories of multiple UAVs, equipable with several and various types of static/movable sensors, to detect a target within a given search area. The framework is implemented in xDEVS and automatically deploys models over a set of containers in the Google Cloud Platform using the Google Kubernetes Engine. The results of the paper show that our deployment policy allows us to speed up the computation up to 3.35 times and lets the operator simultaneously optimize several search strategies to determine the most appropriate for the given scenario.

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