Particle swarm optimisation algorithms and their application to controller design for flexible structure systems

Particle swarm optimisation (PSO) is one of the relatively new optimisation techniques, which has become increasingly popular in tuning and designing controllers for different applications. A major problem is that simple PSO have a tendency to converge to local optima, mainly, due to lack of diversity in the particles as the algorithm proceeds and improper selection of other parameters. Maintaining diversity within a population is challenging for PSO, especially for dynamic problems. In order to increase diversity in the search space and to improve convergence, a new variant of PSO is proposed. The increased interest from industry and real-world applications has led to several modifications in the conventional algorithms so as to deal with multiple conflicting objectives and constraints. A modified multi-objective PSO (MOPSO) proposal is made which will allow the algorithm to deal with multi-objective optimisation problems. The main challenge, in designing a MOPSO algorithm, is to select local and global best for each particle so as to obtain a wide range of solutions that trade-off among the conflicting objectives. In the proposed algorithm, a new technique is introduced that combines external archive and non-dominated fronts of the current population in order to select the global best for each particle. The effectiveness of the proposed algorithm is assessed with two examples in controller design for vibration control of flexible structure systems and satisfactory results have been obtained

Integrating continuous differential evolution with discrete local search for meander line RFID antenna design

The automated design of meander line RFID antennas is a discrete self-avoiding walk(SAW) problem for which efficiency is to be maximized while resonant frequency is to beminimized. This work presents a novel exploration of how discrete local search may beincorporated into a continuous solver such as differential evolution (DE). A prior DE algorithmfor this problem that incorporates an adaptive solution encoding and a bias favoringantennas with low resonant frequency is extended by the addition of the backbite localsearch operator and a variety of schemes for reintroducing modified designs into the DEpopulation. The algorithm is extremely competitive with an existing ACO approach and thetechnique is transferable to other SAW problems and other continuous solvers. The findingsindicate that careful reintegration of discrete local search results into the continuous populationis necessary for effective performance

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

Human Factors-Based Many-Objective Personnel Recruitment for Safety-Critical Work Environments

In spite of many improvements in industrial safety of the last decades, nowadays four people per minute die in the world for occupational illnesses and accidents at work. Besides equipping machines with the most advanced technologies, industrial safety has become more and more interested in human factors in recent years, since many accidents at work are proven to be blamed on dangerous behaviours of workers. Recruiting workers with proper risk perception and caution can increase how safely they will deal with the task assigned, thus reducing devastating events. This paper presents a many-objective optimization framework for personnel recruitment in safety-critical work environments. Four objectives are considered: cost and learning time (which are minimized), and risk perception and caution (which are maximized). A neural network-based module computes each candidate’s risk perception and caution for every single task he/she applies for. Pareto optimal solutions are generated using the Multi-Objective Particle Swarm Optimizer based on hypervolume (MOPSOhv). The best personnel recruitment is selected by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The effectiveness of the proposed framework was validated on two real-world recruitment processes involving 100 and 300 candidates, respectively

Integrated optimisation for dynamic modelling, path planning and energy management in hybrid race vehicles

Simulation software has for many years been developed to enhance the research and development phase of new vehicle introductions. With the introduction of the testing embargo in most forms of world championship motorsport, model validation is a necessity. To optimise the unknown vehicle and tyre parameters and to reduce the error between measured and simulated data in such a multi-input multi-output non-convex optimisation problem, a novel multi-objective particle swarm optimisation (PSO) technique is applied to ensure a fully validated vehicle model is developed and analysed for speed and performance. These optimisation algorithms are further developed to explore the trajectory planning problem to improve the lap time for the shortest path, minimum curvature and a combined approach, producing optimal racing line pathways and vehicle dynamic inputs and output responses by exploring trajectories and vehicle traction circle limits. Finally, a hybrid electric vehicle transient dynamics model for the control of energy management is presented. The hybrid powertrain contains an internal combustion engine, kinetic energy recovery system and heat energy recovery system with deployment and harvesting control parameters. The performance of single-objective and multi-objective particle swarm optimisation algorithms are compared and analysed. The proposed simulation model and optimisation techniques are applied to address an array of problems, including model validation, racing line trajectory design, fastest lap time problem, and energy management strategies. All results are validated and optimised with respect to the experimental data collected on the real track in Silverstone to ensure the results can be applied to physical real-world scenarios

On the Design of a Novel Solid Oxide Fuel Cell Combined Cooling, Heating and Power System for UK Residential Needs

Combined cooling, heating and power (CCHP) systems have become a topic of increasing research interest especially now that they may offer substantial improvements for conservation of fuel and electrical power in the domestic residential sector. However, only a few of the fuel cell (FC)-based CCHP systems have considered the inclusion of other power sources as part of their design with respect to diverse criteria for system optimisation. Most of the research undertaken thus far has focused on the performance improvement of CCHP systems when operated as a single energy source and has not considered the operation when connected to the electrical power distribution grid or under dynamic load conditions. The aim of this research project is to design a solid oxide fuel cell (SOFC)-based CCHP hybrid system that maximises system efficiency and minimises emissions and system costs in an objective manner with minimal operator and customer intervention. A new system structure has been designed to improve the flexibility of the system such that its functioning is closer to practical applications in both island and grid-connected modes, and still returns optimised performance with no need for system redesign or reconfiguration. A novel combination of grey relationship analysis (GRA) linked to an entropy weighting approach has been developed to evaluate the sizing values of fuel cells, heat exchangers and absorption chillers to improve the technical, economic and environmental system performance and reduce subjectivity and inaccuracy that could be imported through reliance on subjective human judgement. A new algorithm, denoted as the multi-objective particle swarm optimisation (MOPSO)-GRA has been designed to reduce local optimisation problem caused by standard MOPSO algorithms. The proposed system has been verified with published experimental results and comparative analysis has been carried out to verify the advance and the new algorithms. The main conclusion is that the optimum design of the SOFC-based CCHP hybrid system delivers optimised performance in terms of efficiency, operation and through life economy as well as environmental impact that gives a high degree of flexible compatibility within the energy supply environment in the UK

Reliability-based design optimization under mixed aleatory/epistemic uncertainties : theory and applications

Reliability-based design optimization (RBDO) is a well-known design strategy in engineering. However, RBDO usually requires uncertainties to be modeled by statistical distributions. This requires the availability of sufficient sample size so that these variables can be represented accurately by probabilistic distributions. In the design of new systems and structures, usually there is a lack of information about some uncertain variables or parameters and only a reduced set of samples might be available. This prevents their treatment as probability distributions. This type of uncertainty is called epistemic uncertainty. This paper proposes two effective multiobjective evolutionary algorithms to solve design problems under both types of uncertainty: aleatory and epistemic. Two objective functions, namely the cost of the structures and the probability of failure, are considered. The results are Pareto fronts with a trade-off between cost and reliability associated with a specified level of confidence. Pareto fronts show minimum achievable values for the probability of failure for a given cost. The effect of the epistemic uncertainty on the solution is also investigated. An analytical example and two structural examples are solved to show the applicability of the approach and how epistemic uncertainty may affect the results