使用多目标区域化贝叶斯优化的猪饲料设计
Swine Diet Design using Multi-objective Regionalized Bayesian Optimization
AI总结：
本研究提出多目标区域化贝叶斯优化方法，通过划分搜索空间提升猪饲料设计中Pareto解的质量与多样性，较标准方法更优且加速优化过程。
摘要：
动物营养背景下的饲料设计是一个复杂问题，旨在制定具有成本效益的配方，同时平衡最低营养含量。基于代谢反应理论模型和原料中可消化能量浓度的传统方法，在纳入影响动物性能的动物技术或环境变量以及包含与可持续发展政策一致的多目标方面面临局限性。近年来，多目标贝叶斯优化已被提出作为一种有前景的启发式替代方案，能够处理多种信息源的组合、多个且多样的目标，并具有处理测量中不确定性的内在能力，这些不确定性可能与原料营养含量的变异性有关。然而，贝叶斯优化在高维搜索空间中遇到困难，导致探索主要发生在边界处。本研究分析了一种将搜索空间划分为提供局部候选解区域的策略，称为多目标区域化贝叶斯优化，作为在猪饲料设计背景下提高贝叶斯优化提供的Pareto集和Pareto前沿近似质量的替代方案。结果表明，与标准多目标贝叶斯优化相比，这种区域化方法产生了更多样化的非支配解。此外，区域化策略在找到优于文献中引用的随机规划方法所识别解的解决方案方面，效果提高了四倍。使用每轮迭代的查询候选解批次进行的实验表明，优化过程也可以加速，而不会在优化的初始最关键阶段损害Pareto集近似的质量。
英文摘要：
The design of food diets in the context of animal nutrition is a complex problem that aims to develop cost-effective formulations while balancing minimum nutritional content. Traditional approaches based on theoretical models of metabolic responses and concentrations of digestible energy in raw materials face limitations in incorporating zootechnical or environmental variables affecting the performance of animals and including multiple objectives aligned with sustainable development policies. Recently, multi-objective Bayesian optimization has been proposed as a promising heuristic alternative able to deal with the combination of multiple sources of information, multiple and diverse objectives, and with an intrinsic capacity to deal with uncertainty in the measurements that could be related to variability in the nutritional content of raw materials. However, Bayesian optimization encounters difficulties in high-dimensional search spaces, leading to exploration predominantly at the boundaries. This work analyses a strategy to split the search space into regions that provide local candidates termed multi-objective regionalized Bayesian optimization as an alternative to improve the quality of the Pareto set and Pareto front approximation provided by BO in the context of swine diet design. Results indicate that this regionalized approach produces more diverse non-dominated solutions compared to the standard multi-objective Bayesian optimization. Besides, the regionalized strategy was four times more effective in finding solutions that outperform those identified by a stochastic programming approach referenced in the literature. Experiments using batches of query candidate solutions per iteration show that the optimization process can also be accelerated without compromising the quality of the Pareto set approximation during the initial, most critical phase of optimization.
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