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).

在整个科学，工程和经济学的广度上，艰难的优化问题无处不在。例如，在供水系统计划和管理中，水公司通常有兴趣优化其基础设施的几种系统绩效指标，以便提供能够应对并从中断中应对和恢复的可持续和韧性的水/废水服务，以及气候变化和人群增加带来的更广泛的挑战。作为一门经典的学科，在优化方面都取得了重大进步。但是，几乎所有传统的优化求解器，从经典方法到自然风格的计算智能技术，都忽略了一些重要的事实：（i）很少出现现实世界中的优化问题； （ii）人造系统旨在解决其一生中的大量问题，其中许多是重复性或固有相关的。取而代之的是，优化是作为“一次性”过程运行的，即每次都假设零的先验知识从头开始。因此，从解决不同（但可能相关的）优化练习（以前完成的或目前正在进行的）方面的知识/经验，这对于增强目前的目标优化任务很有用。尽管贝叶斯优化考虑将某些决策者的知识纳入了先验，但随后在优化过程中收集的经验被丢弃。在这种情况下，我们不能指望其能力具有经验的任何自动增长。从认知的角度来看，这种做法是违反直觉的，即人类通常通过逐渐积累解决问题的经验并利用现有知识来解决新的未见任务，从新手到领域专家的经常成长。在机器学习中，从相关源任务中获得的利用知识以改善新任务的学习被称为转移学习，这是一个新兴领域，在广泛的应用领域中已经看到了相当大的成功。已经尝试在进化计算中应用转移学习，但它们不认为优化是闭环系统。此外，在优化后已经丢弃了解决问题的练习中的复发模式，因此不能随着时间的推移积累经验。拟议的研究将开发一种革命性的通用优化器（称为转移优化系统），将能够从先前的优化过程中学习知识/经验，然后自主地将这些知识转移到新的不参与的知识中，以使新的知识转移到新的优化任务中。转移优化系统将自适应自动化放置在开发过程的核心，并在几个学科的十字路口探索新的协同作用，包括自然启发的计算，机器学习，人类计算机的交互和高性能并行计算。这些产出将带来行业的自动化，包括优化/缩短的生产周期，资源消耗减少以及更加平衡和创新的产品，这具有巨大的潜力，可以带来经济节省和营业额的增加。拟议的方法将由工业伙伴进行严格评估，首先是在水行业，并将扩展到寄宿生范围的一系列行业，这些部门将优化置于其常规生产/管理过程的核心（例如，可再生能源，医疗保健，汽车，汽车，设备和药品制造商）。

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

Transfer Bayesian Optimization for Expensive Black-Box Optimization in Dynamic Environment

• DOI: 10.1109/smc52423.2021.9659200
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
• 作者: Chen, Renzhi;Li, Ke
• 通讯作者: Li, Ke

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

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