On the Integrity of Performance Comparison for Evolutionary Multi-objective Optimisation Algorithms

This paper proposes the notion that the experimental results and performance analyses of newly developed algorithms in the field of multi-objective optimisation may not offer sufficient integrity for hypothesis testing. This is demonstrated through the multiple comparison of three implementations of the popular Non-dominated Sorting Genetic Algorithm II (NSGA-II) from well-regarded frameworks using the hypervolume indicator. The results show that of the thirty considered comparison cases, only four indicate that there was no significant difference between the performance of either implementation

Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm

The residual stresses induced during shaping and machining play an important role in determining the integrity and durability of metal components. An important issue of producing safety critical components is to find the machining parameters that create compressive surface stresses or minimise tensile surface stresses. In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The new method employs a hierarchical optimisation structure to improve the modelling efficiency, where two learning mechanisms cooperate together: NSGA-II is used to improve the model’s structure while the gradient descent method is used to optimise the numerical parameters. This hybrid approach is then successfully applied to the problem that concerns the prediction of machining induced residual stresses in aerospace aluminium alloys. Based on the developed reliable prediction models, NSGA-II is further applied to the multi-objective optimal design of aluminium alloys in a ‘reverse-engineering’ fashion. It is revealed that the optimal machining regimes to minimise the residual stress and the machining cost simultaneously can be successfully located

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system

For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system

Identifying and Detecting Attacks in Industrial Control Systems

The integrity of industrial control systems (ICS) found in utilities, oil and natural gas pipelines, manufacturing plants and transportation is critical to national wellbeing and security. Such systems depend on hundreds of field devices to manage and monitor a physical process. Previously, these devices were specific to ICS but they are now being replaced by general purpose computing technologies and, increasingly, these are being augmented with Internet of Things (IoT) nodes. Whilst there are benefits to this approach in terms of cost and flexibility, it has attracted a wider community of adversaries. These include those with significant domain knowledge, such as those responsible for attacks on Iran’s Nuclear Facilities, a Steel Mill in Germany, and Ukraine’s power grid; however, non specialist attackers are becoming increasingly interested in the physical damage it is possible to cause. At the same time, the approach increases the number and range of vulnerabilities to which ICS are subject; regrettably, conventional techniques for analysing such a large attack space are inadequate, a cause of major national concern. In this thesis we introduce a generalisable approach based on evolutionary multiobjective algorithms to assist in identifying vulnerabilities in complex heterogeneous ICS systems. This is both challenging and an area that is currently lacking research. Our approach has been to review the security of currently deployed ICS systems, and then to make use of an internationally recognised ICS simulation testbed for experiments, assuming that the attacking community largely lack specific ICS knowledge. Using the simulator, we identified vulnerabilities in individual components and then made use of these to generate attacks. A defence against these attacks in the form of novel intrusion detection systems were developed, based on a range of machine learning models. Finally, this was further subject to attacks created using the evolutionary multiobjective algorithms, demonstrating, for the first time, the feasibility of creating sophisticated attacks against a well-protected adversary using automated mechanisms

Safety system design optimisation using a multi-objective genetic algorithm

This paper describes a design optimisation process applied to systems that require a high likelihood of functioning on demand. It is imperative that the best use of the available resources is made and an optimal rather than just an adequate system design is produced. The contribution of this research is in the development of an integrated approach which not only considers the primary system objective, availability of the system, but caters for all critical factors imperative to obtain an optimal system design. This research therefore combines the latest advantages of the fault tree analysis technique and the binary decision diagram method along with a multi-objective optimisation approach. The application area is a High Integrity Protection System of an offshore platform. The optimisation criteria involves unavailability, cost, spurious trip frequency and maintenance down time. The results produced using this method are compared to those obtained by exhaustive search

Identifying vulnerabilities of industrial control systems using evolutionary multiobjective optimisation

In this paper, we propose a novel methodology to assist in identifying vulnerabilities in real-world complex heterogeneous industrial control systems (ICS) using two Evolutionary Multiobjective Optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well-known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these vulnerabilities to generate combinatorial attacks. The generated attacks were aimed at compromising the safety of the system and inflicting economic loss. Results were compared against random attacks, and the performance of the EMO algorithms was evaluated using hypervolume, spread, and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using machine learning algorithms. The designed approach was further tested against the developed detection methods. The obtained results demonstrate that the developed EMO approach is a promising tool in the identification of the vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can serve as a proactive defense tool for control and security engineers to identify and prioritise vulnerabilities in the system. The approach can be employed to design resilient control strategies and test the effectiveness of security mechanisms, both in the design stage and during the operational phase of the system

A novel risk assessment and optimisation model for a multi-objective network security countermeasure selection problem

Budget cuts and the high demand in strengthening the security of computer systems and services constitute a challenge. Poor system knowledge and inappropriate selection of security measures may lead to unexpected financial and data losses. This paper proposes a novel Risk Assessment and Optimisation Model (RAOM) to solve a security countermeasure selection problem, where variables such as financial cost and risk may affect a final decision. A Multi-Objective Tabu Search (MOTS) algorithm has been developed to construct an efficient frontier of non-dominated solutions, which can satisfy organisational security needs in a cost-effective manner

An integrated design optimisation approach for systems with dependencies

The design of a safety system is critical if functionality is to be maximised and consequences reduced. There is often a trade off between the performance obtainable and the resources available. To address these balancing issues, which are usually impractical by hand for a designer, multi-objective optimisation techniques can be used. When considering safety systems there is often the situation of dependencies between components, for example with regard to maintenance. To evaluate the system behaviour in these situations an appropriate analysis method is required. The aim of this paper is to present an optimisation approach which integrates traditional methods of system failure evaluation. The combined method uses the fault tree analysis technique to represent the causes of failure on demand of the system, the binary decision diagram and Markov methods for system quantification (for independent and dependent sections of the fault tree respectively), and the Improved Strength Pareto Evolutionary Approach (SPEA2) to find the most optimal design solution. The end product is a mechanism to yield the best design option for safety systems incorporating dependencies. The paper presents the principles of the method and a case study to illustrate how the method is applied. The results produced, along with conclusions are provided