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Ant Colony Optimization (ACO) algorithms which belong to metaheuristic algorithms and swarm intelligence algorithms have been the focus of much attention in the quest to solve optimization problems. These algorithms are inspired by colonies of ants foraging for food from their nest and have been considered state-of-art methods for solving both discrete and continuous optimization problems. One of the most important phases of ACO algorithms is the construction phase during which an ant builds a partial solution and develops a state transition strategy. There have been a number of studies on the state transition strategy. However, most of the research studies look at how to improve pheromone updates rather than at how the ant itself makes a decision to move from a current position to the next position. The aim of this research is to develop a novel state transition strategy for Ant Colony Optimization algorithms that can improve the overall performance of the algorithms. The research has shown that the state transition strategy in ACO can be improved by introducing non-rational decision-making. The new proposed algorithm is called Cognitive Ant Colony Optimization and uses a new concept of decision-making taken from cognitive behaviour theory. In this proposed algorithm, the ACO has been endowed with non-rational behaviour in order to improve the overall optimization behaviour of ants during the process. This new behaviour will use a non-rational model named prospect theory (Kahneman & Tversky, 1979) to select the transition movements of the ants in the colony in order to improve the overall search capability and the convergence of the algorithm. The new Cognitive Ant Colony Optimization framework has been tested on the Travelling Salesman Problem (TSP), Water Distribution System and Continuous optimization problems. The results obtained show that our algorithm improved the performance of previous ACO techniques considerably.

Quantum annealing is the quantum equivalent of the well known classical simulated annealing algorithm for combinatorial optimization problems. Despite the appeal of the approach, quantum annealing algorithms competitive with the state of the art for specific problems hardly exist in the literature. Graph colouring is a difficult problem of practical significance that can be formulated as combinatorial optimization. By introducing a symmetry-breaking problem representation, and finding fast incremental techniques to calculate energy changes, a competitive graph colouring algorithm based on quantum annealing is derived. This algorithm is further enhanced by tuning simplification techniques; replica spacing techniques to increase robustness; and a messaging protocol, which enables quantum annealing to efficiently take advantage of multiprocessor environments. Additionally, observations of some patterns in the tuning for random graphs led to a more effective algorithm able to find new upper bounds for several widely-used benchmark graphs, some of which had resisted improvement in the last two decades.

The ant colony optimization (ACO) metaheuristic is inspired by the foraging behaviour of real ant colonies. Similarly with other metaheuristics, ACO suffers from stagnation behaviour, where all ants construct the same solution from early stages. In result, the solution quality may be degraded because the population may get trapped on local optima. In this thesis, we propose a novel approach, called direct communication (DC) scheme, that helps ACO algorithms to escape from a local optimum if they get trapped. The experimental results on two routing problems showed that the DC scheme is effective. Usually, researchers are focused on problems in which they have static environment. In the last decade, there is a growing interest to apply nature-inspired metaheuristics in optimization problems with dynamic environments. Usually, dynamic optimization problems (DOPs) are addressed using evolutionary algorithms. In this thesis, we apply several novel ACO algorithms in two routing DOPs. The proposed ACO algorithms are integrated with immigrants schemes in which immigrant ants are generated, either randomly or with the use of knowledge from previous environment(s), and replace other ants in the current population. The experimental results showed that each proposed algorithm performs better in different dynamic cases, and that they have better performance than other peer ACO algorithms in general. The existing benchmark generators for DOPs are developed for binary-encoded combinatorial problems. Since routing problems are usually permutation-encoded combinatorial problems, the dynamic environments used in the experiments are generated using a novel benchmark generator that converts a static problem instance to a dynamic one. The specific dynamic benchmark generator changes the fitness landscape of the problem, which causes the optimum to change in every environmental change. Furthermore in this thesis, another benchmark generator is proposed which moves the population to another location in the fitness landscape, instead of modifying it. In this way, the optimum is known and one can see how close to the optimum an algorithm performs during the environmental changes.

In today‟s highly competitive business environment it is vital to have smart and robust decision making framework for companies to be competitive or even to stay in the business. Profit margin increases is no longer a result of producing and bring more products to the market. Instead it is also a result of reducing cost, in particular tooling cost. In order to succeed with this, industry need to look in to innovative intelligent systems to enhance their process development so that maximum utilisation of tools can be achieved. Tooling is part of a process hence having an optimal process design is one ideal strategy for best utilising of tools. In design optimisation however presence of uncertainty in design variables and in the mathematical model (used for representing the real life process) is inevitable. For reliable design solution to be found this process complexity need to be addressed. The aim of this research is to understand work roll system optimisation and thermal issues within rolling system design, understand uncertainties and sources of uncertainties and develop Genetic Algorithm (GA) based solution frameworks so that a conscious decision, that prolong tool life can be made. The thesis has proposed a framework for generating approximate models from numeric finite element (FE) data. Using the proposed framework a number of quantitative work roll system thermal analysis and optimisation models were generated and used in subsequent optimisation process. In the absence of a suitable multi-pass model that exhibits the features of a multi-stage process; confident decision making will not be possible. Hence the research has developed a quantitative multi-pass model to simulate the work roll system thermal analysis and optimisation problem that represents the relationships as well as inherited features between passes. The research has developed a Genetic Algorithm based optimisation framework that deals with the constraint quantitative problem as well as the uncertainty, in the design space and fitness function. The research also proposed a post GA result analysis methodology for identifying the final best optimal design solution for the research many objective high dimensional work roll system problems in presence of uncertainty The performance of the proposed frameworks was studied and analysed through case studies. The research also identifies future research directions in the subject area.

There is a growing recognition for the multiple criteria decision making (MCDM) based multi-objective evolutionary algorithms (MOEAs) for tackling many-objective optimisation problems (MaOPs). In that, the aim is to utilise the decision makers’ (DMs’) preferences to guide the search towards a few solutions, as against the whole Pareto-optimal front (POF). This thesis is based on the premise that the practical utility of the MCDM based MOEAs may be impaired due to the lack of–objectivity (a rational basis); repeatability (identical preferences for identical options); consistency (alike preferences across multiple interaction stages); and coherence (alike preferences by multiple DMs) in the DMs’ preferences. To counter these limitations, this thesis aimed at developing offline and online decision support by capturing the preference-structure of the objective functions inherent in the problem model itself. This aim has been realised through the following objectives: • Identification of a criterion for the decision support: in that, preservation of the correlation– structure of the MOEA solutions is found to be a robust criterion in the case of MaOPs. • Development of a machine learning based offline objective reduction framework: it com¬prises of linear and nonlinear objective reduction algorithms, which facilitate the decision support through revelation of: (i) the redundant objectives (if any), (ii) preference-ranking of the essential objectives, (iii) the smallest objective sets corresponding to pre-specified errors, and (iv) the objective sets of pre-specified sizes that correspond to minimum error. • Development of an online objective reduction framework: it addresses a major pitfall associated with the offline framework, that–an essential objective if erroneously eliminated as redundant, has no scope of being reconsidered in the subsequent analysis. This pitfall is countered through a probabilistic retention of all the objectives, and this serves as a self-correcting mechanism that enhances the overall accuracy. • Timing the decision support: it is acknowledged that the revelations by the decision support may vary depending on when the offline or online framework is applied during an MOEA run. This uncertainty on the timing of the decision support is countered through the proposition of an entropy based dissimilarity measure. The efficacy of the proposed frameworks is investigated against a broad range of test problems (scaled up to 50 objectives) and some real-world MaOPs; and, the accuracy of the corresponding decision support is compared against that of an alternative approach based on preserving the dominance relations. The results illustrate that the proposed frameworks and the corresponding decision support bear significant utility for those MaOPs, where not all the objectives are essential, or equally important for describing the true POF. The considered real-world problems also bear evidence of the fact that MaOPs with redundant and disparately important objectives may commonly exist in practice.

The desire to optimise problems is as prevalent in today's society as it has ever been. The demand for increases in speed and efficiency is relentless and has resulted in the need for mathematical models to bear greater resemblance to real-life situations. This focus on increased realism has paved the way for new dynamic variants to classic optimisation problems. This thesis begins by considering the Dynamic Vehicle Routing Problem. The basic premise of this routing problem is as follows a percentage of customers are known a priori, for which routes are constructed, further customers then arrive during the course of the working day and need to be incorporated into an evolving schedule. Literature has proposed a timeslot approach, whereby one partitions the working day into a series of smaller problems, that one is then required to solve in succession. This technique is used to produce a variety of metaheuristics based implementations, most noticeably Ant Colony Optimisation and Tabu Search. Consideration is then given to the Dynamic Vehicle Routing Problem with Time Windows. This problem is similar to the Dynamic Vehicle Routing Problem, but requires each customer to be serviced within a predefined period of the day. A metaheuristic approach adapted from the most successful algorithm implemented on the Dynamic Vehicle Routing Problem is presented. Finally consideration is given to a time-based decomposition technique for the Vehicle Routing Problems with Time Windows (Large-Scale instances). This work makes use of the dynamic solution technique developed in the preceding work, and is used in conjunction with an Ant Colony Optimisation algorithm and a descent algorithm.

This research focuses on nature-inspired optimisation algorithms, in particular, the Particle Swarm Optimisation (PSO) Algorithm and the Bees Algorithm. The PSO Algorithm is a population-based stochastic optimisation technique first invented in 1995. It was inspired by the social behaviour of birds flocking or a school of fish. The Bees Algorithm is a population-based search algorithm initially proposed in 2005. It mimics the food foraging behaviour of swarms of honey bees. The thesis presents three algorithms. The first algorithm called the PSO-Bees Algorithm is a cross between the PSO Algorithm and the Bees Algorithm. The PSO-Bees Algorithm enhanced the PSO Algorithm with techniques derived from the Bees Algorithm. The second algorithm called the improved Bees Algorithm is a version of the Bees Algorithm that incorporates techniques derived from the PSO Algorithm. The third algorithm called the SNTO-Bees Algorithm enhanced the Bees Algorithm using techniques derived from the Sequential Number-Theoretic Optimisation (SNTO) Algorithm. To demonstrate the capability of the proposed algorithms, they were applied to different optimisation problems. The PSO-Bees Algorithm is used to train neural networks for two problems, Control Chart Pattern Recognition and Wood Defect Classification. The results obtained and those from tests on well known benchmark functions provide an indication of the performance of the algorithm relative to that of other swarm-based stochastic optimisation algorithms. The improved Bees Algorithm was applied to mechanical design optimisation problems (design of welded beams and coil springs) and the mathematical benchmark problems used previously to test the PSO-Bees Algorithm. The algorithm incorporates cooperation and communication between different neighbourhoods. The results obtained show that the proposed cooperation and communication strategies adopted enhanced the performance and convergence of the algorithm. The SNTO-Bees Algorithm was applied to a set of mechanical design optimisation problems (design of welded beams, coil springs and pressure vessel) and mathematical benchmark functions used previously to test the PSO-Bees Algorithm and the improved Bees Algorithm. In addition, the algorithm was tested with a number of deceptive multi modal benchmark functions. The results obtained help to validate the SNTO-Bees Algorithm as an effective global optimiser capable of handling problems that are deceptive in nature with high dimensions.

Solving real-world combinatorial problems is involved in many industry fields to minimise operational cost or to maximise profit, or both. Along with continuous growth in computing power, many asset management decision-making processes that were originally solved by hand now tend to be based on big data analysis. Larger scale problem can be solved and more detailed operation instructions can be delivered. In this thesis, we investigate models and algorithms to solve large scale Geographically Distributed asset Maintenance Problems (GDMP). Our study of the problem was motivated by our business partner, Gaist solutions Ltd., to optimise scheduling of maintenance actions for a drainage system in an urban area. The models and solution methods proposed in the thesis can be applied to many similar issues arising in other industry fields. The thesis contains three parts. We firstly built a risk driven model considering vehicle routing problems and the asset degradation information. A hyperheuristic method embedded with customised low-level heuristics is employed to solve our real-world drainage maintenance problem in Blackpool. Computational results show that our hyperheuristic approach can, within reasonable CPU time, produce much higher quality solutions than the scheduling strategy currently implemented by Blackpool council. We then attempt to develop more efficient solution approaches to tackle our GDMP. We study various hyperheuristics and propose efficient local search strategies in part II. We present computational results on standard periodic vehicle routing problem instances and our GDMP instances. Based on manifold experimental evidences, we summarise the principles of designing heuristic based solution approaches to solve combinatorial problems. Last bu not least, we investigate a related decision making problem from highway maintenance, that is again of interest to Gaist solutions Ltd. We aim to make a strategical decision to choose a cost effective method of delivering the road inspection at a national scale. We build the analysis based on the Chinese Postman Problem and theoretically proof the modelling feasibility in real-world road inspection situations. We also propose a novel graph reduction process to allow effective computation over very large data sets.

In this thesis we present a new stochastic optimisation model arising from supplyside management of power networks. We provide the exact optimal solution under assumption that the environment is Markovian. For the semi-Markovian environment we establish existence of an optimal policy in an important subclass of policies. Finally, we solve the problem for a number of particular examples of environment.