Investigating evolutionary computation with smart mutation for three types of Economic Load Dispatch optimisation problem

The Economic Load Dispatch (ELD) problem is an optimisation task concerned with how electricity generating stations can meet their customers’ demands while minimising under/over-generation, and minimising the operational costs of running the generating units. In the conventional or Static Economic Load Dispatch (SELD), an optimal solution is sought in terms of how much power to produce from each of the individual generating units at the power station, while meeting (predicted) customers’ load demands. With the inclusion of a more realistic dynamic view of demand over time and associated constraints, the Dynamic Economic Load Dispatch (DELD) problem is an extension of the SELD, and aims at determining the optimal power generation schedule on a regular basis, revising the power system configuration (subject to constraints) at intervals during the day as demand patterns change. Both the SELD and DELD have been investigated in the recent literature with modern heuristic optimisation approaches providing excellent results in comparison with classical techniques. However, these problems are defined under the assumption of a regulated electricity market, where utilities tend to share their generating resources so as to minimise the total cost of supplying the demanded load. Currently, the electricity distribution scene is progressing towards a restructured, liberalised and competitive market. In this market the utility companies are privatised, and naturally compete with each other to increase their profits, while they also engage in bidding transactions with their customers. This formulation is referred to as: Bid-Based Dynamic Economic Load Dispatch (BBDELD). This thesis proposes a Smart Evolutionary Algorithm (SEA), which combines a standard evolutionary algorithm with a “smart mutation” approach. The so-called ‘smart’ mutation operator focuses mutation on genes contributing most to costs and penalty violations, while obeying operational constraints. We develop specialised versions of SEA for each of the SELD, DELD and BBDELD problems, and show that this approach is superior to previously published approaches in each case. The thesis also applies the approach to a new case study relevant to Nigerian electricity deregulation. Results on this case study indicate that our SEA is able to deal with larger scale energy optimisation tasks

Otimização de proteções balísticas de baixo peso para absorção de energia

Technology advances continue to revolutionise military equipment. The development of new firepower induces an interest in the enhancement of protection gear, both for transportation vehicles and personnel. There has been a significant amount of research of methods to increase protection capabilities without increases in the weight of a given defence system. This dissertation seeks to develop an optimisation tool that results in light-weight armour plates without compromising protection capabilities. A thorough study on the propagation of elastic and plastic stress waves aims for a better understanding of how an armour system behaves upon ballistic impact. The first part of this dissertation focuses on the development of a Python script that provides an efficient approach to model generation in Abaqus. It enables the user to avoid time consuming actions when designing ballistic test models to later simulate through the software. This script is also used to validate the theory behind elastic and plastic stress wave propagation while also being able to access output databases and interpret obtained results. The importance of the script is relevant for the second part of the dissertation, which takes advantage of the Abaqus Python Application Programming interface (API) to perform optimisation procedures automatically. Focusing particularly on the application of the particle swarm optimisation algorithm, this work continuously improves the efficiency and accuracy of the mentioned algorithm by dividing three different optimisation problems into several experiments. Each one of the experiments is carefully defined to highlight the impact of a specific operating parameter of the algorithm. A validation of the stress wave propagation and how it is affected upon contact with layered media is carefully conducted through a series of different analysis approaches. It is shown that the plastic stress wave propagates slower than the elastic one and that plastic deformation affects the properties of the generated stress wave, such as wavelength. The implemented particle swarm optimisation algorithm proved to be an effective approach to problem solving, however, for complex problems the operational parameters must be carefully chosen.Os avanços na tecnologia continuam a revolucionar equipamentos militares. O desenvolvimento de novas armas de fogo induz interesse no aprimoramento de equipamento de proteção, para veículos de transporte e pessoal. Tem havido uma quantidade significativa de investigação de métodos para aumentar as capacidades de proteção sem aumento de peso de um dado sistema de proteção. Esta dissertação tem como objetivo o desenvolvimento de uma ferramenta de otimização que resulta em placas de armadura de baixo peso sem comprometer capacidades de proteção. Um estudo cuidadoso acerca da propagação de ondas de tensão elásticas e plásticas procura compreender a forma como um sistema de armadura reage após um impacto balístico. A primeira parte desta dissertação foca-se no desenvolvimento de um código em Python que fornece uma abordagem eficiente à geração de modelos no Abaqus. Isto permite que o utilizador evite ações que consumam tempo ao criar modelos de teste balístico para simular mais tarde através do software. Este código é também usado para validar a teoria por detrás da propagação de ondas de tensão elásticas e plásticas e ao mesmo tempo habilitar o acesso a dados de saída do software e interpretar resultados obtidos. A importância do código é relevante para a segunda parte da dissertação, que tira vantagem da interface de aplicação e programação do Abaqus Python (API) para executar procedimentos de otimização de forma automática. Com foco em particular na aplicação do algoritmo de otimização por enxame de partículas, este trabalho melhora continuamente a eficácia e precisão do algoritmo mencionado através da divisão de três diferentes problemas de otimização em várias experiências. Cada uma das experiências é cuidadosamente definida para destacar o impacto de um parâmetro operacional específico do algoritmo. A validação da propagação da onda de tensão e como é afetada após contacto com um meio material de múltiplas camadas é cuidadosamente estudada através de séries de diferentes análises. É mostrado que a onda de tensão plástica se propaga mais lentamente que a elástica e que deformação plástica afeta as propriedades da onda de tensão gerada, tal como o comprimento de onda. O algoritmo de otimização por enxame de partículas implementado prova ser uma abordagem eficaz para a resolução de problemas, no entanto, para problemas complexos os parâmetros operacionais devem ser escolhidos com cuidado.Mestrado em Engenharia Mecânic

A Plant Propagation Algorithm for Constrained Engineering Optimisation Problems

Optimisation problems arising in industry are some of the hardest, often because of the tight specifications of the products involved. They are almost invariably constrained and they involve highly nonlinear, and non-convex functions both in the objective and in the constraints. It is also often the case that the solutions required must be of high quality and obtained in realistic times. Although there are already a number of well performing optimisation algorithms for such problems, here we consider the novel Plant Propagation Algorithm (PPA) which on continuous problems seems to be very competitive. It is presented in a modified form to handle a selection of problems of interest. Comparative results obtained with PPA and state-of-the-art optimisation algorithms of the Nature-inspired type are presented and discussed. On this selection of problems, PPA is found to be as good as and in some cases superior to these algorithms.</jats:p

Multi-Objective Global Pattern Search: Effective numerical optimisation in structural dynamics

With this work, a novel derivative-free multi-objective optimisation approach for solving engineering problems is presented. State-of-the-art algorithms usually require numerical experimentation in order to tune the algorithm’s multiple parameters to a specific optimisation problem. This issue is effectively tackled by the presented deterministic method which has only a single parameter. The most popular multi-objective optimisation algorithms are based on pseudo-random numbers and need several parameters to adjust the associated probability distributions. Deterministic methods can overcome this issue but have not attracted much research interest in the past decades and are thus seldom applied in practice. The proposed multi-objective algorithm is an extension of the previously introduced deterministic single-objective Global Pattern Search algorithm. It achieves a thorough recovery of the Pareto frontier by tracking a predefined number of non-dominated samples during the optimisation run. To assess the numerical efficiency of the proposed method, it is compared to the well-established NSGA2 algorithm. Convergence is demonstrated and the numerical performance of the proposed optimiser is discussed on the basis of several analytic test functions. Finally, the optimiser is applied to two structural dynamics problems: transfer function estimation and finite element model updating. The introduced algorithm performs well on test functions and robustly converges on the considered practical engineering problems. Hence, this deterministic algorithm can be a viable and beneficial alternative to random-number-based approaches in multi-objective engineering optimisation

Metaheuristics for Transmission Network Expansion Planning

This chapter presents the characteristics of the metaheuristic algorithms used to solve the transmission network expansion planning (TNEP) problem. The algorithms used to handle single or multiple objectives are discussed on the basis of selected literature contributions. Besides the main objective given by the costs of the transmission system infrastructure, various other objectives are taken into account, representing generation, demand, reliability and environmental aspects. In the single-objective case, many metaheuristics have been proposed, in general without making strong comparisons with other solution methods and without providing superior results with respect to classical mathematical programming. In the multi-objective case, there is a better convenience of using metaheuristics able to handle conflicting objectives, in particular with a Pareto front-based approach. In all cases, improvements are still expected in the definition of benchmark functions, benchmark networks and robust comparison criteria

A multi-cycled sequential memetic computing approach for constrained optimisation

In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles. Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded. In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles. The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions. The comparison against some well-known algorithms showed the superiority of the developed algorithm in terms of the consumed fitness evaluations and the solution quality

Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering

History matching production data in finite difference reservoir simulation models has been and always will be a challenge for the industry. The principal hurdles that need to be overcome are finding a match in the first place and more importantly a set of matches that can capture the uncertainty range of the simulation model and to do this in as short a time as possible since the bottleneck in this process is the length of time taken to run the model. This study looks at the implementation of Particle Swarm Optimisation (PSO) in history matching finite difference simulation models. Particle Swarms are a class of evolutionary algorithms that have shown much promise over the last decade. This method draws parallels from the social interaction of swarms of bees, flocks of birds and shoals of fish. Essentially a swarm of agents are allowed to search the solution hyperspace keeping in memory each individual’s historical best position and iteratively improving the optimisation by the emergent interaction of the swarm. An intrinsic feature of PSO is its local search capability. A sequential niching variation of the PSO has been developed viz. Flexi-PSO that enhances the exploration and exploitation of the hyperspace and is capable of finding multiple minima. This new variation has been applied to history matching synthetic reservoir simulation models to find multiple distinct history 3 matches to try to capture the uncertainty range. Hierarchical clustering is then used to post-process the history match runs to reduce the size of the ensemble carried forward for prediction. The success of the uncertainty modelling exercise is then assessed by checking whether the production profile forecasts generated by the ensemble covers the truth case