非线性系统
数学优化
计算机科学
非线性规划
软件
人口
组分(热力学)
线性规划
期限(时间)
整数规划
最优化问题
运筹学
数学
量子力学
热力学
物理
社会学
人口学
程序设计语言
作者
Cassidy K. Buhler,Hande Y. Benson
出处
期刊:Cornell University - arXiv
日期:2023-08-22
标识
DOI:10.48550/arxiv.2308.11549
摘要
Protected areas (PAs) are designated spaces where human activities are restricted to preserve critical habitats. Decision-makers are challenged with balancing a trade-off of financial feasibility with ecological benefit when establishing PAs. Given the long-term ramifications of these decisions and the constantly shifting environment, it is crucial that PAs are carefully selected with long-term viability in mind. Using AI tools like simulation and optimization is common for designating PAs, but current decision models are primarily linear. In this paper, we propose a derivative-free optimization framework paired with a nonlinear component, population viability analysis (PVA). Formulated as a mixed integer nonlinear programming (MINLP) problem, our model allows for linear and nonlinear inputs. Connectivity, competition, crowding, and other similar concerns are handled by the PVA software, rather than expressed as constraints of the optimization model. In addition, we present numerical results that serve as a proof of concept, showing our models yield PAs with similar expected risk to that of preserving every parcel in a habitat, but at a significantly lower cost. The overall goal is to promote interdisciplinary work by providing a new mathematical programming tool for conservationists that allows for nonlinear inputs and can be paired with existing ecological software. Our code and data are available at https://github.com/cassiebuhler/conservation-dfo.
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