Advanced p-Metric Based Many-Objective Evolutionary Algorithm

Evolutionary many objective based optimization has been gaining a lot of attention from the evolutionary computation researchers and computational intelligence community. Many of the state-of-the-art multi-objective and many-objective optimization problems (MOPs, MaOPs) are inefficient in maintaining the convergence and diversity performances as the number of objectives increases in the modern-day real-world applications. This phenomenon is obvious indeed as Pareto-dominance based EAs employ non-dominated sorting which fails considerably in providing enough convergent pressure towards the Pareto front (PF). Researchers invested much more time and effort in addressing this issue by improving the scalability in MaOPs and they have come up with non-Pareto-dominance-based EAs such as decomposition-based, indicator-based and reference-based approaches. In addition to that, the algorithm has to account for the additional computational budget. This thesis proposes an advanced polar-metric (p-metric) based Many-objective EA (in short APMOEA) for tackling both MOPs and MaOPs. p-metric, a recently proposed performance based visualization metric, employs an array of uniformly, distributed direction vectors. In APMOEA, a two-phase selection scheme is employed which combines both non-dominated sorting and p-metric. Moreover, this thesis also proposes a modified P-metric methodology in order to adjust the direction vectors dynamically. In the experiments, we compare APMOEA with four state-of-the-art Many-objective EAs under, three performance indicators. According to the empirical results, APMOEA shows much improved performances on most of the test problems, involving both MOPs and MaOPs.Electrical Engineerin

Creating an Objective Methodology for Human-Robot Team Configuration Selection

As technology has been advancing and designers have been looking to future applications, it has become increasingly evident that robotic technology can be used to supplement, augment, and improve human performance of tasks. Team members can be combined in various combinations to better utilize their capabilities and skills to create more efficient and diversified operational teams. A primary obstacle to integrating new robotic technology has been the inability to quantitatively compare overall team performance between very different team configurations without limiting the analysis to a few metrics. To-date, mission designers have arbitrarily assigned importance to mission parameters, subjectively limiting the search space. While this has been effective at evaluating individual mission plans, the arbitrary evaluation criteria has made a straightforward comparison between different research projects and ranking scales impossible. The question then becomes how to select an objective set of criteria for any given problem. It is this final question that this research sought to answer. A methodology was developed to facilitate performance comparison amongst heterogeneous human and robot teams. This methodology makes no assumptions about mission priorities or preferences. Instead, it provides an objective, generic, quantitative method to reduce the complexity of the mission designer's decision space. It employs an heuristic, greedy objective reduction algorithm to reduce problem complexity and a multi-objective genetic algorithm to explore the design space. The human-robot team configuration selection problem was utilized as the application that motivated this research. The methodology, however, will be applicable to a wider domain of research. It will provide a structure to enable broader search of the design space, exploration of the differences between performance metrics, and comparison of optimization models that facilitate evaluation of the design options

Evolutionary many-objective optimisation: pushing the boundaries

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonMany-objective optimisation poses great challenges to evolutionary algorithms. To start with, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimisation, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between proximity and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimisation algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. In this thesis, we address these challenges, aiming to make evolutionary algorithms as effective in many-objective optimisation as in two- or three-objective optimisation. First, we significantly enhance Pareto-based algorithms to make them suitable for many-objective optimisation by placing individuals with poor proximity into crowded regions so that these individuals can have a better chance to be eliminated. Second, we propose a grid-based evolutionary algorithm which explores the potential of the grid to deal with many-objective optimisation problems. Third, we present a bi-goal evolution framework that converts many objectives of a given problem into two objectives regarding proximity and diversity, thus creating an optimisation problem in which the objectives are the goals of the search process itself. Fourth, we propose a comprehensive performance indicator to compare evolutionary algorithms in optimisation problems with various Pareto front shapes and any objective dimensionality. Finally, we construct a test problem to aid the visual investigation of evolutionary search, with its Pareto optimal solutions in a two-dimensional decision space having similar distribution to their images in a higher-dimensional objective space. The work reported in this thesis is the outcome of innovative attempts at addressing some of the most challenging problems in evolutionary many-objective optimisation. This research has not only made some of the existing approaches, such as Pareto-based or grid-based algorithms that were traditionally regarded as unsuitable, now effective for many-objective optimisation, but also pushed other important boundaries with novel ideas including bi-goal evolution, a comprehensive performance indicator and a test problem for visual investigation. All the proposed algorithms have been systematically evaluated against existing state of the arts, and some of these algorithms have already been taken up by researchers and practitioners in the field.Department of Computer Science, Brunel University Londo

Optimising the sustainability of blockchain-based systems: balancing environmental sustainability, decentralisation and trustworthiness

Blockchain technology is an emerging technology revolutionising information technology and represents a change in how information is shared. It has captured the interest of several disciplines because it promises to provide security, anonymity and data integrity without any third-party control. Although blockchain technology has great potential for the construction of the future of the digital world, it is facing a number of technical challenges. A most critical concern is related to its environmental sustainability. It has been acknowledged that blockchain-based systems' energy consumption and carbon emissions are massive and can affect their sustainability. Therefore, optimising the environmental sustainability of these systems is necessary. Several studies have been proposed to mitigate this issue. However, the literature needs to include models for optimising the environmental sustainability of blockchain-based systems without compromising the fundamental properties inherent in blockchain technology. In this context, this thesis aims to optimise the environmental sustainability of blockchain-based systems by balancing different conflicting objectives without compromising the decentralisation and trustworthiness of the systems. First of all, we reformulate the problem of the environmental sustainability of the systems as a search-based software engineering problem. We represent the problem as a subset selection problem that selects an optimal set of miners for mining blocks in terms of four conflicting objectives: energy consumption, carbon emissions, decentralisation and trustworthiness. Secondly, we propose a reputation model to determine reputable miners based on their behaviour in a blockchain-based system. The reputation model can support the enhancement of the environmental sustainability of the system. Moreover, it can improve the system's trustworthiness when the number of miners is reduced to minimise energy consumption and carbon emissions. Thirdly, we propose a self-adaptive model that optimises the environmental sustainability of blockchain-based systems taking into account environmental changes and decision-makers' requirements. We have conducted a series of experiments to evaluate the applicability and effectiveness of the proposed models. Finally, the results demonstrate that our models can enhance the environmental sustainability of blockchain-based systems without compromising the core properties of blockchain technology

User-preference based evolutionary algorithms for many-objective optimisation

Evolutionary Algorithms (EA) have enjoyed great success in finding solutions for multi-objective problems that have two or three-objectives in the past decade. The majority of these Evolutionary Multi-objective Optimisation (EMO) algorithms explored the decision-space using the selection pressure governed methods that are based on dominance relation. Although these algorithms are effective locating solutions for multi-objective problems, they have not been very successful for problem instances having more than three objectives, usually named as many-objective problems. The main reason behind this shortcoming is the fact that the dominance comparison becomes ineffective as the number of objectives increases. In this thesis, we incorporate some user-preference methods into EMO algorithms to enhance their ability to handle many-objective problems. To this end, we introduce a distance metric derived from user-preference schemes such as the reference point method and light beam search found in multi-criteria decision making. This distance metric is used to guide the EMO algorithm to locate solutions within certain areas of the objective-space known as preferred regions. In our distance metric approach, the decision maker is allowed to specify the amount of spread of solutions along the solution front as well. We name this distance metric based EMO algorithm as d-EMO, which is a generalised framework that can be constructed using any EA. This distance metric approach is computationally less expensive as it does not rely on dominance ranking methods, but very effective in solving many-objective problems. One key issue that remains to be resolved is that there are no suitable metrics for comparing the performance of these user-preference EMO algorithms. Therefore, we introduce a variation of the normalised Hyper-Volume (HV) metric suitable for comparing user-preference EMO algorithms. The key feature in our HV calculation process is to consider only the solutions within each preferred region. This methodology favours user-preference EMO algorithms that have converged closely to the Pareto front within a preferred region. We have identified two real-world engineering design problems in optimising aerofoil and lens designs, and formulated them as many-objective problems. The optimisation process of these many-objective problems is computationally expensive. Hence, we use a reference point PSO algorithm named MDEPSO to locate solutions effectively in fewer function evaluations. This PSO algorithm is less prone to getting stuck in local optimal fronts and still retains its fast convergence ability. In MDEPSO, this feature is achieved by generating leader particles using a differential evolution rule rather than picking particles directly from the population or an external archive. The main feature of the optimisation process of these aerofoil and lens design problems is the derivation of reference points based on existing designs. We illustrate how these existing designs can be used to either obtain better or new design solutions that correspond to various requirements. This process of deriving reference points based on existing design models, and integrating them into a user-preference EMO framework is a novel approach in the optimisation process of such computationally expensive engineering design problems