Transfer Optimisation System for Adaptive Automated Nature-Inspired Optimisation

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中，水公司通常对优化其基础设施的几个系统性能指标感兴趣。他们特别感兴趣的是提供可持续和有弹性的水/废水服务，能够应对并从中断中恢复，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）人工系统的设计目的是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“一次性”过程运行的，即每次从头开始假设零先验知识。因此，解决不同（但可能相关）优化练习（无论是先前完成的还是当前正在进行的）的知识/经验将被浪费，这些知识/经验可能对增强手头的目标优化任务很有用。虽然贝叶斯优化考虑将决策者的一些知识作为先验，但在优化过程中收集的经验在之后被丢弃。在这种情况下，我们不能指望他们的能力随着经验自动增长。从认知的角度来看，这种做法是反直觉的，因为人类通常是通过逐渐积累解决问题的经验和利用现有知识来处理新的看不见的任务，从新手成长为领域专家的。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，在广泛的应用领域取得了相当大的成功。已经有一些将迁移学习应用于进化计算的尝试，但他们没有把优化作为一个闭环系统来考虑。此外，问题解决练习中的重复模式在优化后被丢弃，因此经验不能随时间积累。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），该优化器将能够从先前的优化过程中学习知识/经验，然后在开放式动态环境中连续，自主和有选择地将这些知识转移到新的未知优化任务中。转移优化系统将自适应自动化置于开发过程的核心，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，减少资源消耗和更平衡和创新的产品，这些产品具有巨大的潜力，可以节省经济和增加营业额。提议的方法将由工业合作伙伴进行严格评估，首先是在水务行业，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/管理过程的核心（例如软件工程，可再生能源，医疗保健，汽车，电器和药品制造商）。

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. They are particularly interested in providing 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 a transfer optimisation system) that will be able to learn knowledge/experience from previous optimisation process and then continuously, autonomously, and selectively transfer such knowledge to new unseen optimisation tasks in open-ended dynamic environments. 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. software engineering, renewable energy, healthcare, automotive, appliance and medicine manufacturers).