Multi-objective optimisation of safety-critical hierarchical systems

Achieving high reliability, particularly in safety critical systems, is an important and often mandatory requirement. At the same time costs should be kept as low as possible. Finding an optimum balance between maximising a system's reliability and minimising its cost is a hard combinatorial problem. As the size and complexity of a system increases, so does the scale of the problem faced by the designers. To address these difficulties, meta-heuristics such as Genetic Algorithms and Tabu Search algorithms have been applied in the past for automatically determining the optimal allocation of redundancies in a system as a mechanism for optimising the reliability and cost characteristics of that system. In all cases, simple reliability block diagrams with restrictive assumptions, such as failure independence and limited 2-state failure modes, were used for evaluating the reliability of the candidate designs produced by the various algorithms.This thesis argues that a departure from this restrictive evaluation model is possible by using a new model-based reliability evaluation technique called Hierachically Performed Hazard Origin and Propagation Studies (HiP-HOPS). HiP-HOPS can overcome the limitations imposed by reliability block diagrams by providing automatic analysis of complex engineering models with multiple failure modes. The thesis demonstrates that, used as the fitness evaluating component of a multi-objective Genetic Algorithm, HiP-HOPS can be used to solve the problem of redundancy allocation effectively and with relative efficiency. Furthermore, the ability of HiP-HOPS to model and automatically analyse complex engineering models, with multiple failure modes, allows the Genetic Algorithm to potentially optimise systems using more flexible strategies, not just series-parallel. The results of this thesis show the feasibility of the approach and point to a number of directions for future work to consider

Multiobjective Reliability Allocation in Multi-State Systems: Decision Making by Visualization and Analysis of Pareto Fronts and Sets

ISBN 978-1-4471-2206-7Reliability-based design, operation and maintenance of multi-state systems lead to multiobjective (multicriteria) optimization problems whose solutions are represented in terms of Pareto Fronts and Sets. Among these solutions, the decision maker must choose the ones which best satisfy his\her preferences on the objectives of the problem. Visualization and analysis of the Pareto Fronts and Sets can help decision makers in this task. In this view, a recently introduced graphical representation, called Level Diagrams, is here used in support of the analysis of Pareto Fronts and Sets aimed at reducing the number of non-dominated solutions to be considered by the decision maker. Each objective and design parameter is represented on separate "synchronized" diagrams which position the Pareto front points according to their proximity to ideal preference points and on the basis of this representation a two-step front reduction procedure is proposed. An application to a redundancy allocation problem of literature concerning a multi-state system is used to illustrate the analysis

A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Assessing optimal water quality monitoring network in road construction using integrated information-theoretic techniques

Author´s accepted manuscript.The environmental impacts of road construction on the aquatic environment necessitate the monitoring of receiving water quality. The main contribution of the paper is developing a feasible methodology for spatial optimization of the water quality monitoring network (WQMN) in surface water during road construction using the field data. First, using the Canadian Council of Ministers of the Environment (CCME) method, the water quality index (WQI) was computed in each potential monitoring station during construction. Then, the integrated form of the information-theoretic techniques consists of the transinformation entropy (TE), and the value of information (VOI) were calculated for the potential stations. To achieve the optimal WQMNs, the Non-dominated Sorting Genetic Algorithm II and III (NSGA-II, and III) based multi-objective optimization models were developed considering three objective functions, including i) minimizing the number of stations, ii) maximizing the VOI in the selected network, and iii) minimizing redundant information for the selected nodes. Finally, three multi-criteria decision-making models, including Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), and Analytical Hierarchy Process (AHP) were utilized for choosing the best alternative among Pareto optimal solutions considering various weighing scenarios assigned to criteria. The applicability of the presented methodology was assessed in a 22 km long road construction site in southern Norway. The results deliver significant knowledge for decision-makers on establishing a robust WQMN in surface water during road construction projects.publishedVersio

Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms

The first author was funded by the Marie Curie Actions of the European Union’s Seventh Framework Programme FP7/2007- 2013/ under REA grant agreement number 607656 (OceaNet project) and by the industrial partner James Fisher Marine Services Ltd. Mojo Maritime (JFMS group) have provided access to Mermaid to support, and for integration with, this research. This work is also funded by the EPSRC (UK) grant for the SuperGen United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1]This is the author accepted manuscriptThis paper explores the use of genetic algorithms to optimize the operation and maintenance (O&M) assets of an offshore wind farm. Three different methods are implemented in order to demonstrate the approach. The optimization problem simultaneously considers both the reliability characteristics of the offshore wind turbines and the composition of the maintenance fleet, seeking to identify the optimal configurations for the strategic assets. These are evaluated in order to minimize the operating costs of the offshore farm while maximizing both its reliability and availability. The considerations used for the application of genetic algorithms as an effective way to support the assets management are described, and a case study to show the applicability of the approach is presented. The variation of the economic performance indicators as a consequence of the optimization procedure are discussed, and the implementation of this method in a wider computational framework for the O&M assets improvement introduced.European CommissionMojo Ocean Dynamics Ltd. T/A Mojo Maritime LtdEPSRC (UK) grant for the SuperGen United Kingdom Centre for Marine Energy Research (UKCMER