Transfer Optimisation System for Adaptive Automated Nature-Inspired Optimisation

用于自适应自动自然优化的传输优化系统

• 批准号: MR/S017062/1
• 负责人: Ke Li
• 金额: $ 139.86万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS017062%2F1
• 关键词: Transfer; Optimisation; System; Adaptive; Automated

Hard optimisation problems are ubiquitous across the breadth of science, engineering and economics. For example, in water system planning and management, water companies are often interested in optimising several system performance measures of their infrastructures in order to provide sustainable and resilient water/wastewater services that are able to cope with and recover from disruption, as well as wider challenges brought by climate change and population increase. As a classic discipline, significant advances in both theory and algorithms have been achieved in optimisation. However, almost all traditional optimisation solvers, ranging from classic methods to nature-inspired computational intelligence techniques, ignore some important facts: (i) real-world optimisation problems seldom exist in isolation; and (ii) artificial systems are designed to tackle a large number of problems over their lifetime, many of which are repetitive or inherently related. Instead, optimisation is run as a 'one-off' process, i.e. it is started from scratch by assuming zero prior knowledge each time. Therefore, knowledge/experience from solving different (but possibly related) optimisation exercises (either previously completed or currently underway), which can be useful for enhancing the target optimisation task at hand, will be wasted. Although the Bayesian optimisation considers incorporating some decision maker's knowledge as a prior, the gathered experience during the optimisation process is discarded afterwards. In this case, we cannot expect any automatic growth of their capability with experience. This practice is counter-intuitive from the cognitive perspective where humans routinely grow from a novice to domain experts by gradually accumulating problem-solving experience and making use of existing knowledge to tackle new unseen tasks. In machine learning, leveraging knowledge gained from related source tasks to improve the learning of the new task is known as transfer learning, an emerging field that considerable success has been witnessed in a wide range of application domains. There have been some attempts on applying transfer learning in evolutionary computation, but they do not consider the optimisation as a closed-loop system. Moreover, the recurrent patterns within problem-solving exercises have been discarded after optimisation, thus experience cannot be accumulated over time.The proposed research will develop a revolutionary general-purpose optimiser (as known as transfer optimisation system) that will be able to learn knowledge/experience from previous optimisation process and then autonomously and selectively transfer such knowledge to new unseen optimisation tasks. The transfer optimisation system places adaptive automation at the heart of the development process and explores novel synergies at the crossroads of several disciplines including nature-inspired computation, machine learning, human-computer interaction and high-performance parallel computing. The outputs will bring automation in industry, including an optimised/shortened production cycle, reduced resource consumption and more balanced and innovative products, which have great potentials to result in economic savings and an increase of turnover. The proposed methods will be rigorously evaluated by the industrial partners, first in water industry and will be expanded to a boarder range of sectors which put the optimisation at the heart of their regular production/management process (e.g. renewable energy, healthcare, automotive, appliance and medicine manufacturers).

硬优化问题在科学、工程和经济学领域无处不在。例如，在水系统规划和管理方面，水务公司通常有兴趣优化其基础设施的几个系统性能指标，以提供可持续和有弹性的水/废水服务，能够科普中断以及气候变化和人口增长带来的更广泛的挑战。作为一门经典学科，优化在理论和算法上都取得了重大进展。然而，几乎所有传统的优化求解器，从经典方法到自然启发的计算智能技术，都忽略了一些重要的事实：（i）现实世界的优化问题很少孤立存在;（ii）人工系统被设计为在其生命周期内解决大量问题，其中许多问题是重复的或内在相关的。相反，优化是作为一个“一次性”过程运行的，即每次都假设零先验知识从头开始。因此，来自解决不同（但可能相关）优化练习（先前完成的或当前正在进行的）的知识/经验将被浪费，这些知识/经验可用于增强手头的目标优化任务。虽然贝叶斯优化考虑将一些决策者的知识作为先验知识，但在优化过程中收集的经验在事后被丢弃。在这种情况下，我们不能指望他们的能力随着经验的增长而自动增长。从认知的角度来看，这种做法是违反直觉的，人类通常通过逐渐积累解决问题的经验，并利用现有的知识来解决新的看不见的任务，从新手成长为领域专家。在机器学习中，利用从相关源任务中获得的知识来改进新任务的学习被称为迁移学习，这是一个新兴领域，在广泛的应用领域中取得了相当大的成功。已经有一些尝试将迁移学习应用于进化计算，但他们没有将优化视为闭环系统。此外，在优化后，解决问题的练习中的重复模式已被丢弃，因此经验不能随着时间的推移而积累。拟议的研究将开发一个革命性的通用优化器（称为转移优化系统），该系统将能够从以前的优化过程中学习知识/经验，然后自主选择性地将这些知识转移到新的看不见的优化任务中。传输优化系统将自适应自动化置于开发过程的核心，并在多个学科的交叉点探索新的协同效应，包括自然启发的计算，机器学习，人机交互和高性能并行计算。这些产出将带来工业自动化，包括优化/缩短生产周期，减少资源消耗以及更平衡和创新的产品，这具有巨大的潜力，从而节省经济成本并增加营业额。工业合作伙伴将对所提出的方法进行严格评估，首先是水行业，然后将扩展到更广泛的行业，这些行业将优化置于其常规生产/管理流程的核心（例如可再生能源，医疗保健，汽车，家电和医药制造商）。

项目成果

Routing-Led Placement of VNFs in Arbitrary Networks

• DOI: 10.1109/cec48606.2020.9185531
• 发表时间: 2020-01
• 期刊: 2020 IEEE Congress on Evolutionary Computation (CEC)
• 作者: Joseph Billingsley;Ke Li;W. Miao;G. Min;N. Georgalas

Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts

• DOI: 10.48550/arxiv.2205.14344
• 发表时间: 2022-05
• 作者: Renzhi Chen;Ke Li

Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings

进化多标准优化 - 第十届国际会议，EMO 2019，美国密歇根州东兰辛，2019 年 3 月 10-13 日，会议记录

• DOI: 10.1007/978-3-030-12598-1_42
• 发表时间: 2019
• 作者: Billingsley J

Surrogate-Assisted Evolutionary Multi-Objective Optimization for Hardware Design Space Exploration

用于硬件设计空间探索的代理辅助进化多目标优化

• 发表时间: 2022
• 作者: Chen R

Transfer Learning-Based Parallel Evolutionary Algorithm Framework for Bilevel Optimization

基于迁移学习的双层优化并行进化算法框架

• DOI: 10.1109/tevc.2021.3095313
• 发表时间: 2022
• 期刊: IEEE Transactions on Evolutionary Computation
• 影响因子: 14.3
• 作者: Chen L