Differential Evolution for Multiobjective Portfolio Optimization

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cannot be used. Stochastic search heuristic can be an attractive alternative. In this paper, we propose a new multiobjective algorithm for portfolio optimization: DEMPO - Differential Evolution for Multiobjective Portfolio Optimization. The main advantage of this new algorithm is its generality, i.e., the ability to tackle a portfolio optimization task as it is, without simplifications. Our empirical results show the capability of our approach of obtaining highly accurate results in very reasonable runtime, in comparison with quadratic programming and another state-of-art search heuristic, the so-called NSGA II.Portfolio Optimization, Multiobjective, Real-world Constraints, Value at Risk, Expected Shortfall, Differential Evolution

MOPO-LSI: A User Guide

MOPO-LSI is an open-source Multi-Objective Portfolio Optimization Library for Sustainable Investments. This document provides a user guide for MOPO-LSI version 1.0, including problem setup, workflow and the hyper-parameters in configurations

Understanding the Electricity-Water-Climate Change Nexus Using a Stochastic Optimization Approach

Climate change has been shown to cause droughts (among other catastrophic weather events) and it is shown to be exacerbated by the increasing levels of greenhouse gas emissions on our planet. In May 2013, CO2 daily average concentration over the Pacific Ocean at Mauna Loa Observatory reached a dangerous milestone of 400 ppm, which has not been experienced in thousands of years in the earth\u27s climate. These levels were attributed to the ever-increasing human activity over the last 5-6 decades. Electric power generators are documented by the U.S. Department of Energy to be the largest users of ground and surface water and also to be the largest emitters of carbon dioxide and other greenhouse gases. Water shortages and droughts in some parts of the U.S. and around the world are becoming a serious concern to independent system operators in wholesale electricity markets. Water shortages can cause significant challenges in electricity production having a direct socioeconomic impact on surrounding regions. Several researchers and institutes around the world have highlighted the fact that there exists an inextricable nexus between electricity, water, and climate change. However, there are no existing quantitative models that study this nexus. This dissertation aims to ll this vacuum. This research presents a new comprehensive quantitative model that studies the electricity-water-climate change nexus. The first two parts of the dissertation focuses on investigating the impact of a joint CO2 emissions and H2O usage tax on a sample electric power network. The latter part of the dissertation presents a model that can be used to study the impact of a joint CO2 and H2O cap-and-trade program on a power grid. We adopt a competitive Markov decision process (CMDP) approach to model the dynamic daily competition in wholesale electricity markets, and solve the resulting model using a reinforcement learning approach. In the first part, we study the impacts of dierent tax mechanisms using exogenous tax rate values found in the literature. We consider the complexities of a electricity power network by using a standard direct-current optimal power flow formulation. In the second part, we use a response surface optimization approach to calculate optimal tax rates for CO2 emissions and H2O usage, and then we examine the impacts of implementing this optimal tax on a power grid. In this part, we use a multi-objective variant of the optimal power flow formulation and solve it using a strength Pareto evolutionary algorithm. We use a 30-bus IEEE power network to perform our detailed simulations and analyses. We study the impacts of implementing the tax policies under several realistic scenarios such as the integration of wind energy, stochastic nature of wind energy, integration of PV energy, water supply disruptions, adoption of water saving technologies, tax credits to generators investing in water saving technologies, and integration of Hydro power generation. The third part, presents a variation of our stochastic optimization framework to model a joint CO2 and H2O cap-and-trade program in wholesale electricity markets for future research. Results from the research show that for the 30-bus power grid, transition from coal generation to wind power could reduce CO2 emissions by 60% and water usage about 40% over a 10-year horizon. Electricity prices increase with the adoption of water and carbon taxes; likewise, capacity disruptions also cause electricity prices to increase

Differential evolution for multiobjective portfolio optimization

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cannot be used. Stochastic search heuristic can be an attractive alternative. In this paper, we propose a new multiobjective algorithm for portfolio optimization: DEMPO - Differential Evolution for Multiobjective Portfolio Optimization. The main advantage of this new algorithm is its generality, i.e., the ability to tackle a portfolio optimization task as it is, without simplifications. Our empirical results show the capability of our approach of obtaining highly accurate results in very reasonable runtime, in comparison with quadratic programming and another state-of-art search heuristic, the so-called NSGA II

Differential Evolution for Multiobjective Portfolio Optimization

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cannot be used. Stochastic search heuristic can be an attractive alternative. In this paper, we propose a new multiobjective algorithm for portfolio optimization: DEMPO - Differential Evolution for Multiobjective Portfolio Optimization. The main advantage of this new algorithm is its generality, i.e., the ability to tackle a portfolio optimization task as it is, without simplifications. Our empirical results show the capability of our approach of obtaining highly accurate results in very reasonable runtime, in comparison with quadratic programming and another state-of-art search heuristic, the so-called NSGA I

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

Emissions modelling for engine cycle and aircraft trajectory optimisation

The aviation industry is currently experiencing a growth rate of about 4% per annum and this trend is expected to continue into the future. One concern about this growth rate is the impact it will have on the environment particularly in terms of emissions of CO2, NOx and relatively recently also cirrus clouds induced by contrails. The ACARE has set emissions reduction targets of 50% reduction of CO2 and noise and 80% reduction of NOx by 2020 relative to Y2000 technology. Clean Sky and other large EU collaborative projects have been launched in an effort to identify new, more efficient, aircraft and engine technologies, greener operational and asset management practices and lower life cycle emissions. This PhD research was funded by and contributed to the Systems for Green Operations Integrated Technology Demonstrator (SGO-ITD) of the Clean Sky project. The key contribution to knowledge of this research is the development and application of a methodology for simultaneous optimisation of aircraft trajectories and engine cycles. Previous studies on aircraft trajectory optimisation studies, published in the public domain, are based on relatively low fidelity models. The case studies presented in this thesis are multi-objective and based on higher fidelity, verified aircraft, engine and emissions models and also include assessments of conceptual engines with conceptual LPP combustors. The first task involved the development of reactor based NOx emission prediction models for a conventional aero gas turbine combustor and a novel conceptual lean pre-mixed pre-vaporised combustor. A persistent contrails prediction model was also developed. A multi-disciplinary framework comprising a genetic algorithm based optimiser integrated with an engine performance, an aircraft performance and an emission prediction model was then developed. The framework was initially used to perform multi-disciplinary aircraft trajectory optimisation studies and subsequently both aircraft trajectory and engine cycle optimisation studies simultaneously to assess trade-offs between mission fuel burn, flight time, NOx production and persistent contrails formation ... [cont.]

Trajectory optimization for high-altitude long endurance UAV maritime radar surveillance

For an unmanned aerial vehicle (UAV) carrying out a maritime radar surveillance mission, there is a tradeoff between maximizing information obtained from the search area and minimizing fuel consumption. This paper presents an approach for the optimization of a UAV's trajectory for maritime radar wide area persistent surveillance to simultaneously minimize fuel consumption, maximize mean probability of detection, and minimize mean revisit time. Quintic polynomials are used to generate UAV trajectories due to their ability to provide complete and complex solutions while requiring few inputs. Furthermore, the UAV dynamics and surveillance mission requirements are used to ensure a trajectory is realistic and mission compatible. A wide area search radar model is used within this paper in conjunction with a discretized grid in order to determine the search area's mean probability of detection and mean revisit time. The trajectory generation method is then used in conjunction with a multi-objective particle swarm optimization (MOPSO) algorithm to obtain a global optimum in terms of path, airspeed (and thus time), and altitude. The performance of the approach is then tested over two common maritime surveillance scenarios and compared to an industry recommended baseline