Abstract
This paper presents an agent-based simulator for environmental land change that includes efficient and parallel auto-tuning. This simulator extends the Multi-Agent System for Environmental simulation (MASE) by introducing rationality to agents using a mentalistic approach—the Belief-Desire-Intention (BDI) model—and is thus named MASE-BDI. Because the manual tuning of simulation parameters is an error-prone, labour and computing intensive task, an auto-tuning approach with efficient multi-objective optimization algorithms is also introduced. Further, parallelization techniques are employed to speed up the auto-tuning process by deploying it in parallel systems. The MASE-BDI is compared to the MASE using the Brazilian Cerrado biome case. The MASE-BDI reduces the simulation execution times by at least 82 × and slightly improves the simulation quality. The auto-tuning algorithms, by evaluating less than 0.00115 % of a search space with 6 million parameter combinations, are able to quickly tune the simulation model, regardless of the objective used. Moreover, the experimental results show that executing the tuning in parallel leads to speedups of approximately 11 × compared to sequential execution in a hardware setting with 16-CPU cores.
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This work was supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES).
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C. Coelho, C.G., Abreu, C.G., Ramos, R.M. et al. MASE-BDI: agent-based simulator for environmental land change with efficient and parallel auto-tuning. Appl Intell 45, 904–922 (2016). https://doi.org/10.1007/s10489-016-0797-8
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DOI: https://doi.org/10.1007/s10489-016-0797-8