Multiobjective genetic programming for financial portfolio management in dynamic environments

Multiobjective (MO) optimisation is a useful technique for evolving portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk. The resulting Pareto front would approximate the risk/reward Efficient Frontier [Mar52], and simplifies the choice of investment model for a given client’s attitude to risk. However, the financial market is continuously changing and it is essential to ensure that MO solutions are capturing true relationships between financial factors and not merely over fitting the training data. Research on evolutionary algorithms in dynamic environments has been directed towards adapting the algorithm to improve its suitability for retraining whenever a change is detected. Little research focused on how to assess and quantify the success of multiobjective solutions in unseen environments. The multiobjective nature of the problem adds a unique feature to be satisfied to judge robustness of solutions. That is, in addition to examining whether solutions remain optimal in the new environment, we need to ensure that the solutions’ relative positions previously identified on the Pareto front are not altered. This thesis investigates the performance of Multiobjective Genetic Programming (MOGP) in the dynamic real world problem of portfolio optimisation. The thesis provides new definitions and statistical metrics based on phenotypic cluster analysis to quantify robustness of both the solutions and the Pareto front. Focusing on the critical period between an environment change and when retraining occurs, four techniques to improve the robustness of solutions are examined. Namely, the use of a validation data set; diversity preservation; a novel variation on mating restriction; and a combination of both diversity enhancement and mating restriction. In addition, preliminary investigation of using the robustness metrics to quantify the severity of change for optimum tracking in a dynamic portfolio optimisation problem is carried out. Results show that the techniques used offer statistically significant improvement on the solutions’ robustness, although not on all the robustness criteria simultaneously. Combining the mating restriction with diversity enhancement provided the best robustness results while also greatly enhancing the quality of solutions

Multi-objective network planning for the integration of electric vehicles as responsive demands

The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.]The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

Multiobjective robustness for portfolio optimization in volatile environments

Multiobjective methods are ideal for evolving a set of portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk, and an investor can choose her preferred point on the risk-return frontier. However, there are no guarantees that a low-risk solution will remain low-risk . if the environment changes, the relative positions of previously identified solutions may alter. A low-risk solution may become high-risk and vice versa. The robustness of a Multiobjective Genetic Programming (MOGP) algorithm such as SPEA2 is vitally important in the context of the real-world problem of portfolio optimisation. We explore robustness in this context, providing new definitions and a statistical measure to quantify the robustness of solutions. A new robustness measure is incorporated into a MOGP fitness function to bias evolution towards more robust solutions. This new system ("R-SPEA2") is compared against the original SPEA2 and we present our results

Robustness of multiple objective GP stock-picking in unstable financial markets: Real-world applications track

Multiple Objective Genetic Programming (MOGP) is a promising stock-picking technique for fund managers, because the Pareto front approximates the risk/reward Efficient Frontier and simplifies the choice of investment model for a given client's attitude to risk. Unfortunately GP solutions don't work well if used in an environment that is different from the training environment, and the financial markets are notoriously unstable, often lurching from one market context to another (e.g. "bull" to "bear"). This turns out to be a hard problem -- simple dynamic adaptation methods are insufficient and robust behaviour of solutions becomes extremely important. In this paper we provide the first known empirical results on the robustness of MOGP solutions in an unseen environment consisting of real-world financial data. We focus on two well-known mechanisms to determine which leads to the more robust solutions: Mating Restriction, and Diversity Preservation. We introduce novel metrics for Pareto front robustness, and a novel variation on Mating Restriction, both based on phenotypic cluster analysis

A multi-objective optimisation approach applied to offshore wind farm location selection

This paper compares the three state-of-the-art algorithms when applied to a real-world case of the wind energy sector. Optimum locations are suggested for a wind farm by considering only Round 3 zones around the UK. The problem comprises of some of the most important techno-economic life cycle cost-related factors, which are modelled using the physical aspects of each wind farm location (i.e., the wind speed, distance from the ports, and water depth), the wind turbine size, and the number of turbines. The model is linked to NSGA II, NSGA III, and SPEA 2 algorithms, to conduct an optimisation search. The performance of these three algorithms is demonstrated and analysed, so as to assess their effectiveness in the investment decision-making process in the wind sector, more importantly, for Round 3 zones. The results are subject to the specifics of the underlying life cycle cost model

A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition

A multi-objective transmission reinforcement planning approach for analysing future energy scenarios in the GB network

A multi-objective transmission reinforcement planning framework has been designed to evaluate the effect of applying a future energy scenario to the Great Britain transmission network. This is achieved by examining the identified nondominated set of transmission reinforcement plans, which alleviate thermal capacity constraints, for the multi-criteria problem of five objectives: investment cost, annual constraint cost saving, annual incremental operation and maintenance cost, outage cost and annual line loss saving. The framework is flexible and utilises a systematic algorithm to generate reinforcement plans and alter the associated reinforcements should they exacerbate thermal constraints; hence a pre-determined set of reinforcements is not required to evaluate a scenario. The reinforcements considered are line addition (single-circuit and double-circuit) and line upgrading through reconductoring. The Strength Pareto Evolutionary Algorithm 2 is utilised to explore varying locations, configurations and capacities of network reinforcement. The solutions produced achieve similar cost savings to solutions created by the transmission network owners, showing the suitability of the approach to provide a useful trade-off analysis of the objectives and to assess the network related thermal and economic impact of future energy scenarios. Here the framework is applied to the 2020 generation mix of the Gone Green scenario developed by National Grid

Evaluation of scalarization methods and NSGA-II/SPEA2 genetic algorithms for multi-objective optimization of green supply chain design

This paper considers supply chain design in green logistics. We formulate the choice of an environmentally conscious chain design as a multi-objective optimization (MOO) problem and approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods as well as with two popular genetic algorithms, NSGA-II and SPEA2. We extend an existing case study of green supply chain design in the South Eastern Europe region by optimizing simultaneously costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. The results show that in the considered case the scalarization methods outperform genetic algorithms in finding efficient solutions and that the CO2 and PM emissions can be lowered by accepting a marginal increase of costs over their global minimum

Evolutionary multiobjective optimization for automatic agent-based model calibration: A comparative study

This work was supported by the Spanish Agencia Estatal de Investigacion, the Andalusian Government, the University of Granada, and European Regional Development Funds (ERDF) under Grants EXASOCO (PGC2018-101216-B-I00), SIMARK (P18-TP-4475), and AIMAR (A-TIC-284-UGR18). Manuel Chica was also supported by the Ramon y Cajal program (RYC-2016-19800).The authors would like to thank the ``Centro de Servicios de Informática y Redes de Comunicaciones'' (CSIRC), University of Granada, for providing the computing resources (Alhambra supercomputer).Complex problems can be analyzed by using model simulation but its use is not straight-forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. We propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent-based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e., the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.Spanish Agencia Estatal de InvestigacionAndalusian GovernmentUniversity of GranadaEuropean Commission PGC2018-101216-B-I00 P18-TP-4475 A-TIC-284-UGR18Spanish Government RYC-2016-1980