The consideration of surrogate model accuracy in single-objective electromagnetic design optimization

The computational cost of evaluating the objective function in electromagnetic optimal design problems necessitates the use of cost-effective techniques. This paper describes how one popular technique, surrogate modelling, has been used in the single-objective optimization of electromagnetic devices. Three different types of surrogate model are considered, namely polynomial approximation, artificial neural networks and kriging. The importance of considering surrogate model accuracy is emphasised, and techniques used to improve accuracy for each type of model are discussed. Developments in this area outside the field of electromagnetic design optimization are also mentioned. It is concluded that surrogate model accuracy is an important factor which should be considered during an optimization search, and that developments have been made elsewhere in this area which are yet to be implemented in electromagnetic design optimization

Scalarizing cost-effective multiobjective optimization algorithms made possible with kriging

The use of kriging in cost-effective single-objective optimization is well established, and a wide variety of different criteria now exist for selecting design vectors to evaluate in the search for the global minimum. Additionly, a large number of methods exist for transforming a multi-objective optimization problem to a single-objective problem. With these two facts in mind, this paper discusses the range of kriging assisted algorithms which are possible (and which remain to be explored) for cost-effective multi-objective optimization

Balancing Exploration and Exploitation using Kriging Surrogate Models in Electromagnetic Design Optimization

The balance between exploration and exploitation is an important issue when attempting to find the global minimum of an objective function. This paper describes how this balance may be carefully controlled when using Kriging surrogate models to approximate the objective function

Modeling uncertain and dynamic casualty health in optimization-based decision support for mass casualty incident response

When designing a decision support program for use in coordinating the response to Mass Casualty Incidents, the modelling of the health of casualties presents a significant challenge. In this paper we propose one such health model, capable of acknowledging both the uncertain and dynamic nature of casualty health. Incorporating this into a larger optimisation model capable of use in real-time and in an online manner, computational experiments examining the effect of errors in health assessment, regular updates of health and delays in communication are reported. Results demonstrate the often significant impact of these factors

Online optimization of casualty processing in major incident response: An experimental analysis

When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility

Agent-based modelling and inundation prediction to enable the identification of businesses affected by flooding

Flooding continues to cause significant disruption to individuals, organisations and communities in many parts of the world. In terms of the impact on businesses in the United Kingdom (UK), flooding is responsible for the loss of millions of pounds to the economy. As part of a UK Engineering and Physical Sciences Research Council funded project on flood risk management, SESAME, research is being carried out with the aim of improving business response to and preparedness for flood events. To achieve this aim, one strand of the research is focused on establishing how agent-based modelling and simulation can be used to evaluate and improve business continuity. This paper reports on the development of the virtual geographic environment (VGE) component of an agent-based model and how this has been combined with inundation prediction to enable the identification of businesses affected by flooding in any urban area of the UK. The VGE has been developed to use layers from Ordnance Survey’s MasterMap, namely the Topography Layer, Integrated Transport Network Layer and Address Layer 2. Coupling the VGE with inundation prediction provides credibility in modelling flood events in any area of the UK. An initial case study is presented focusing on the Lower Don Valley region of Sheffield leading to the identification of businesses impacted by flooding based on a predicted inundation. Further work will focus on the development of agents to model and simulate businesses during and in the aftermath of flood events such that changes in their behaviours can be investigated leading to improved operational response and business continuity

A Scalarizing One-Stage Algorithm for Efficient Multi-Objective Optimization

A novel kriging-assisted algorithm is proposed for computationally expensive multi-objective optimization problems, such as those which arise in electromagnetic design. The algorithm combines the multiple objectives into a single objective, which it then optimizes using a one-stage method from singleobjective optimization. Its efficiency is demonstrated by comparison to a random search on a difficult test function

An Enhanced Probability of Improvement Utility Function for Locating Pareto Optimal Solutions

This paper describes a novel utility function for choosing design vectors to evaluate in multi-objective optimization problems which are statistically most probable to be Pareto-optimal, given the points already evaluated. The method is tunable to the number of existing Pareto-optimal solutions that an unevaluated design vector is sought to dominate, is naturally parallelized, and removes any need for combining the multiple objectives into a single objective with a scalarizing function

Evaluation of centralised and autonomous routing strategies in major incident response

Fast and efficient routing of emergency responders during the response to mass casualty incidents is a critical element of success. However, the predictability of the associated travel times can also have a significant effect on performance during the response operation. This is particularly the case when a decision support model is employed to assist in the allocation of resources and scheduling of operations, as such models typically rely on an ability to make accurate forecasts when evaluating candidate solutions. In this paper we explore how both routing efficiency and uncertainty in travel time prediction are affected by the routing strategy employed. A simulation study is presented, with results indicating that a routing strategy which allows responders to select routes autonomously, as opposed to being instructed via a central decision support program, leads to improvement in overall performance despite the associated increase in uncertainty in travel time prediction

Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident

During a major incident, the emergency services work together to ensure that those casualties who are critically injured are identified and transported to an appropriate hospital as fast as possible. If the incident is multi-site and resources are limited, the efficiency of this process is compromised as the finite resources must be shared among the multiple sites. In this paper, agent-based simulation is used to determine the allocation of resources for a two-site incident which minimizes the latest hospital arrival times for critically injured casualties. Further, how the optimal resource allocation depends on the distribution of casualties across the two sites is investigated. Such application supports the use of agent-based simulation as a tool to aid emergency response