Portfolio management is a multi-period multi-objective optimization problem subject to a wide range of constraints. However, in practice, portfolio management is treated as a single-period problem partly due to the computationally burdensome hyper-parameter search procedure needed to construct a multi-period Pareto frontier. This study presents the Pareto Driven Surrogate (ParDen-Sur) modelling framework to efficiently perform the required hyper-parameter search. ParDen-Sur extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in Evolutionary Algorithms (EAs) alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal Multi-Objective (MO) EAs on two datasets for both the single- and multi-period use cases. Our results show that ParDen-Sur can speed up the exploration for optimal hyper-parameters by almost 2x with a statistically significant improvement of the Pareto frontiers, across multiple EAs, for both datasets and use cases.

The complexity of Chemical Plant Systems (CPS) makes optimising their design and operation challenging tasks. This complexity also results in analytical and numerical simulation models of these systems having high computational costs. Research demonstrates the benefits of using machine learning models as surrogates or substitutes for these computationally expensive simulation models during CPS optimisation. This paper presents the results of our study, extending recent research into optimising chemical plant design and operation. The study explored the original Surrogate Assisted Genetic Algorithms (SA-GA) in more complex variants of the plant design and operation optimisation problem. The more complex plant design variants include additional parallel and feedback components. The study also proposes a novel multivariate extension, Surrogate Assisted NSGA (SA-NSGA), to the original univariate SA-GA algorithm. The study evaluated the SA-NSGA extension on the popular Pressure Swing Adsorption (PSA) system. This paper outlines our extensive experimentation, comparing various meta-heuristic optimisation techniques and numerous machine learning models as surrogates. The results in both more complex plant design variants and the PSA case show the suitability of Genetic Algorithms combined with surrogate models as an optimisation framework for CPS design and operation in single and multi-objective scenarios. The analysis further confirms that combining a Genetic Algorithm framework with Machine Learning Surrogate models as a substitute for long-running simulation models yields significant computational efficiency improvements, 1.7 - 1.84 times speedup for the increased complexity examples and a 2.7 times speedup for the Pressure Swing Adsorption system. The discussion successfully concludes that surrogate assisted Evolutionary Algorithms can be scaled to increasingly complex CPS with parallel and feedback components.

Traditional portfolio management methods can incorporate specific investor preferences but rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL) methods do not rely on these explicit forecasts and are better suited for multi-stage decision processes. To address limitations of the evaluated research, experiments were conducted on three markets in different economies with different overall trends. By incorporating specific investor preferences into our RL models’ reward functions, a more comprehensive comparison could be made to traditional methods in risk-return space. Transaction costs were also modelled more realistically by including nonlinear changes introduced by market volatility and trading volume. The results of this study suggest that there can be an advantage to using RL methods compared to traditional convex mean-variance optimisation methods under certain market conditions. Our RL models could significantly outperform traditional single-period optimisation (SPO) and multi-period optimisation (MPO) models in upward trending markets, but only up to specific risk limits. In sideways trending markets, the performance of SPO and MPO models can be closely matched by our RL models for the majority of the excess risk range tested. The specific market conditions under which these models could outperform each other highlight the importance of a more comprehensive comparison of Pareto optimal frontiers in risk-return space. These frontiers give investors a more granular view of which models might provide better performance for their specific risk tolerance or return targets.