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The transmission of power across the GB transmission system, as operated by National Grid, results in inevitable loss of electrical power. Operationally these power losses cannot be eliminated, but they can be reduced by adjustment of the system voltage profile. At present the minimisation of active power losses relies upon a lengthy manually based iterative adjustment process. Therefore the system operator requires the development of advanced optimisation tools to cope with the challenges faced over the next decade, such as achieving the stringent greenhouse gas emission targets laid down by the UK government, while continue to provide an economical, secure and efficient service. To meet these challenges the research presented in this thesis has developed optimisation techniques that can assist control centre engineers by automatically setting up voltage studies that are low loss and low cost. The proposed voltage optimisation techniques have been shown to produce solutions that are secured against 800 credible contingency cases. A prototype voltage optimisation tool has been deployed, which required the development of a series of novel approaches to extend the functionality of an existing optimisation program. This research has lead to the development of novel methods for handling multi-objectives, contradictory shunt switching configurations and selecting all credible contingencies. Studies indicate that a theoretical loss saving of 1.9% is achievable, equivalent to an annual emissions saving of approximately 64,000 tonnes of carbon dioxide. A novel security constrained mixed integer non-linear optimisation technique has also been developed. The proposed method has been shown to be superior to several conventional methods on a wide range of IEEE standard network models and also on a range of large-scale GB network models. The proposed method manages to further reduce active power losses and also satisfies all security constraints.

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This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective space. Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization techniques when the search space is of high dimension and contains many local minima and maxima. Likewise, MOEAs are most interesting when applied to non-intuitive complex systems. But, these systems are often computationally expensive to calculate. When these systems require independent computations to evaluate each objective, the computational expense grows with each additional objective. This method has developed methods that reduces the time required for evolution by reducing the number of objective evaluations, while still evolving solutions that are Pareto optimal. To date, all other Multiple Objective Evolutionary Algorithms (MOEAs) require the evaluation of all objectives before a fitness value can be assigned to an individual. The original contributions of this thesis are: 1. Development of a hierarchical search space description that allows association of crossover and mutation settings with elements of the genotypic description. 2. Development of a method for parallel evaluation of individuals that removes the need for delays for synchronization. 3. Dynamical evolution of thresholds for objectives to allow partial evaluation of objectives for individuals. 4. Dynamic objective orderings to minimize the time required for unnecessary objective evaluations. 5. Application of MOEAs to the computationally expensive flare pattern design domain. 6. Application of MOEAs to the optimization of fielded missile warning receiver algorithms. 7. Development of a new method of using MOEAs for automatic design of pattern recognition systems.

The application of optimization to Water Distribution Systems encompasses the use of computer-based techniques to problems of many different areas of system design, maintenance and operational management. As well as laying out the configuration of new WDS networks, optimization is commonly needed to assist in the rehabilitation or reinforcement of existing network infrastructure in which alternative scenarios driven by investment constraints and hydraulic performance are used to demonstrate a cost-benefit relationship between different network intervention strategies. Moreover, the ongoing operation of a WDS is also subject to optimization, particularly with respect to the minimization of energy costs associated with pumping and storage and the calibration of hydraulic network models to match observed field data. Increasingly, Evolutionary Optimization techniques, of which Genetic Algorithms are the best-known examples, are applied to aid practitioners in these facets of design, management and operation of water distribution networks as part of Decision Support Systems (DSS). Evolutionary Optimization employs processes akin to those of natural selection and “survival of the fittest” to manipulate a population of individual solutions, which, over time, “evolve” towards optimal solutions. Such algorithms are characterized, however, by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of water networks incurs significant computational overheads, can limit the applicability and scalability of this technology in this domain. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to Water Distribution Systems. A number of new procedures are presented for improving the performance of such algorithms when applied to complex engineering problems. These techniques approach the problem of minimising the impact of the inherent computational complexity of these problems from a number of angles. A novel genetic representation is presented which combines the algorithmic simplicity of the classical binary string of the Genetic Algorithm with the performance advantages inherent in an integer-based representation. Further algorithmic improvements are demonstrated with an intelligent mutation operator that “learns” which genes have the greatest impact on the quality of a solution and concentrates the mutation operations on those genes. A technique for implementing caching of solutions – recalling the results for solutions that have already been calculated - is demonstrated to reduce runtimes for Genetic Algorithms where applied to problems with significant computation complexity in their evaluation functions. A novel reformulation of the Genetic Algorithm for implementing robust stochastic optimizations is presented which employs the caching technology developed to produce an multiple-objective optimization methodology that demonstrates dramatically improved quality of solutions for given runtime of the algorithm. These extensions to the Genetic Algorithm techniques are coupled with a supporting software library that represents a standardized modelling architecture for the representation of connected networks. This library gives rise to a system for distributing the computational load of hydraulic simulations across a network of computers. This methodology is established to provide a viable, scalable technique for accelerating evolutionary optimization applications.

The objectives of this research are threefold: (i) to develop a conceptual framework and a prototype in order to extend the application capability of a category of multiple objective decision support systems (MODSS) techniques; (ii) to explore the combined functionalities of knowledge-based expert systems (ES) and MODSS through embedding an intelligent front-end, and (iii) to develop a new system and process of dealing with multiple objective decision making (MODM) models in a group decision support system (GDSS) framework. Ultimately, a system that integrates MODSS, ES and GDSS is generated, which is then evaluated in a laboratory experimental setup. This integrated system contains a sufficient number of MODM methods to solve MODM problems, provides an ES-based guide to select and use the most suitable MODM method, and has the capability to aggregate individual decision makers' preferences to produce a compromise solution of an MODM problem in different forms and styles of group meetings. The system is supported by a set of group decision making (GDM) methods which combine the preferences of the individual group members and thus increases the confidence of each group member in the compromise solution.The research is conducted using a multiple-methodologies approach using the system development methodology as the backbone. The conceptual framework of the integrated system is elaborated to integrate multiple system elements into one facility at the application system level based on functional and resource integration. A prototype implements this conceptual framework as an intelligence-based and graphical user interface (GUI)-based MODSS that works in an individual/group environment. Both the conceptual framework and the prototype are called Intelligent Multiple Objectives Group Decision Support Systems (IMOGDSS).Initial evaluation of the IMOGDSS is encouraging, which ++ / is conducted in the form of testing a number of hypotheses in an experimental setup. This research thus makes contributions in both theoretical and application domains. Five major contributions are listed below:It develops a unique conceptual framework of integrating MODSS, ES and GDSS effectively to deal with MODM problem in individual/group decision making under a knowledge-based intelligent architecture.It provides a new application of ES, that is, utilising knowledge-based ES to select the most efficient MODM method for each particular decision maker (or decision group) in a particular decision problem.The complete method management function of the MODM methodology base guides the decision makers to use the most suitable method to solve their decision making problems, allows them to use multiple methods to resolve complex problems, that could not otherwise be solved with a single MODM, and also allows the group members to get solutions from different methods.This study produces an opportunity to select and apply the 'best' aggregation model to aggregate the individual solutions of an MODM problem through integrating various GDM methods in a methodology base.This study implements a two-stage configuration of group decision support software that provides a GUI-based hierarchical procedure for solving MODM problems with intelligent guidance in a decision group. The two-stage group decision making procedure is able to help the decision makers to analyse, understand and interact cooperatively in the group decision making process to reach a compromise solution.

Most research studies in the batch process control problem are focused on optimizing system performance. The methods address the problem by minimizing single criterion such as cycle time and tardiness, or bi-criteria such as cycle time and tardiness, and earliness and tardiness. This research demonstrates the use of Stochastic Utility Evaluation (SUE) function approach to optimize system performance using multiple criteria. In long production cycles, the earliness and tardiness weight (utility) of products vary depending on the time. As the time approaches the due-date, it affects contractual penalties, loss of customer goodwill and the storage period for the completed products. It is necessary to reflect the weight of products for earliness and tardiness at decision epochs to decide on the optimal strategy. This research explores how stochastic utility function using stochastic information can be derived and used to strategically improve existing approaches for the batch process control problem. This research first explores how SUE function can be applied to existing model for bi-objective problem such as cycle time and tardiness. Benchmark strategies using SUE function (NACH-SUE, MBS-SUE, No idle and full batch) are compared to each other. The experimental results show that NACH-SUE effectively improves mean cycle time and tardiness performance respectively than other benchmark strategies. Next, SUE function for earliness and tardiness is used in an existing model to develop a tri-objective problem. Typically, this problem is very complex to solve due to its trade-off relationship. However SUE function makes it relatively easy to solve the tri-objective problem since SUE function can be incorporated in an existing model. It is observed that SUE function can be effectively used for solving a tri-objective problem. Performance improvement for averaged value of cycle time, earliness and tardiness is observed under a comprehensive set of experimental conditions.

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