Academic literature on the topic 'Multi-component optimisation problems'

The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once a plateau is reached, the iterative search of the TTP solution space is restarted by using a new initial TSP tour. We propose to make the search more efficient though an adaptive surrogate model (based on a customised form of Support Vector Regression) that learns the characteristics of initial TSP tours that lead to good TTP solutions. The model is used to filter out non-promising initial TSP tours, in effect reducing the amount of time spent to find a good TTP solution. Experiments on a broad range of benchmark TTP instances indicate that the proposed approach filters out a considerable number of non-promising initial tours, at the cost of missing only a small number of the best TTP solutions.

The turbine tip geometry can significantly alter the performance of the turbine stage; its design represents a challenge for a variety of reasons. Multiple disciplines are involved in its design and their requirements limit the creativity of the designer. Multi-Disciplinary Design Optimisation (MDO) offers the capability to improve the performance whilst satisfying all the design constraints. This paper presents a novel design of a turbine tip achieved via MDO techniques. A fully parametrised Computer-Aided Design (CAD) model of the turbine rotor is used to create the squealer geometry and to control the location of the cooling and dust holes. A Conjugate Heat Transfer Computational Fluid Dynamics (CFD) analysis is performed for evaluating the aerothermal performance of the component and the temperature the turbine operates at. A Finite Element (FE) analysis is then performed to find the stress level that the turbine has to withstand. A bi-objective optimisation reduces simultaneously the aerodynamic loss and the stress level. The Multipoint Approximation Method (MAM) recently enhanced for multi-objective problems is chosen to solve this optimisation problem. The paper presents its logic in detail. The novel geometry offers a significant improvement in the aerodynamic performance whilst reducing the maximum stress. The flow associated with the new geometry is analysed in detail to understand the source of the improvement.

Water distribution systems (WDSs) contribute to undesirable greenhouse gas (GHG) emissions that are generated through their component fabrication, construction, operation and disposal processes. The concentration of GHGs in the atmosphere is strongly associated with global warming and climate change. In order to meet the consequent challenge of limiting GHG emissions, the problem of WDS redesign is formulated here as a multi-objective optimisation problem. The three objectives are as follows: (1) minimisation of total redesign cost, (2) maximisation of the WDS resilience, and (3) minimisation of GHG emissions. The resilience index serves as a measure of the WDS's intrinsic capability to ensure continuity of supply to users after sudden failure conditions, whilst the GHG emissions serve as a measure of environmental performance and climate change mitigation. The output from the non-dominated sorting genetic algorithm (NSGA2) optimisation process is a Pareto front containing optimal solutions traded-off in terms of the three objectives analysed. This methodology was applied on the New York Tunnels and the Anytown Network problems. The results obtained demonstrate that the redesign approach leads to cost-effective and resilient solutions that can also mitigate climate change compared with the single-objective (least cost) and other multi-objective redesigns over the long-term planning horizon.

Motor end cover mounting fracture is a problem recently encountered by novel pure electric vehicles. Regarding the study of the traditional vehicle engine mount bracket and on the basis of the methods of design and optimisation available, we have analysed and optimised the pure electric vehicle end cover mount system. Multi-body dynamic software and finite element software have been combined. First, we highlight the motor end cover mount bracket fracture engineering problems, analyse the factors that may produce fracture, and propose solutions. By using CATIA software to establish a 3D model of the power train mount system, we imported it into ADAMS multi-body dynamic software, conducted 26 condition analysis, obtained five ultimate load conditions, and laid the foundations for subsequent analysis. Next, a mount and shell system was established by the ANSYS finite element method, and modal, strength, and fatigue analyses were performed on the end cover mount. We found that the reason for fracture lies in the intensity of the end cover mount joint, which leads to the safety factor too small and the fatigue life not being up to standard. The main goal was to increase the strength of the cover mount junction, stiffness, safety coefficient, and fatigue life. With this aim, a topology optimisation was conducted to improve the motor end cover. A 3D prototype was designed accordingly. Finally, stiffness, strength, modal, and fatigue were simulated. Our simulation results were as follows. The motor end cover suspension stiffness increases by 20%, the modal frequency increases by 2.3%, the quality increases by 3%, the biggest deformation decreases by 52%, the maximum stress decreases by 28%, the minimum safety factor increases by 40%, and life expectancy increases 50-fold. The results from sample and vehicle tests highlight that the component fracture problem has been successfully solved and the fatigue life dramatically improved.

AbstractThis work presents the Topology Optimisation of the Morphing Variable Span of Tapered Wing (MVSTW) using a finite element method. This topology optimisation aims to assess the feasibility of internal wing components such as ribs, spars and other structural components. This innovative approach is proposed for the telescopic mechanism of the MVSTW, which includes the sliding of the telescopically extended wing into the fixed wing segment. The optimisation is performed using the tools within ANSYS Mechanical, which allows the solving of topology optimisation problems. This study aims to minimise overall structural compliance and maximise stiffness to enhance structural performance, and thus to meet the structural integrity requirements of the MVSTW. The study evaluates the maximum displacements, stress and strain parameters of the optimised variable span morphing wing in comparison with those of the original wing. The optimised wing analyses are conducted on four wingspan extensions, that is, 0%, 25%, 50% and 75%, of the original wingspan, and for different flight speeds to include all flight phases (17, 34, 51 and 68m/s, respectively). Topology optimisation is carried out on the solid wing built with aluminium alloy 2024-T3 to distribute the wing components within the fixed and moving segments. The results show that the fixed and moving wing segments must be designed with two spar configurations, and seven ribs with their support elements in the high-strain area. The fixed and moving wing segments’ structural weight values were reduced to 16.3 and 10.3kg from 112 to 45kg, respectively. The optimised MVSTW was tested using different mechanical parameters such as strains, displacements and von Misses stresses. The results obtained from the optimised variable span morphing wing show the optimal mechanical behaviour and the structural wing integrity needed to achieve the multi-flight missions.

Abstract Background and Aims Best practice for treatment of patients with chronic heart failure involves beta-blockers and renin-angiotensin-aldosterone system inhibitors (RAASi) such as ACE inhibitors (ACE-i), angiotensin receptor blockers (ARB), mineralocorticoid antagonists (MRA) and neprolysin inhibitor/ARB. However, use of these agents, and optimisation of their dosage, is frequently limited by hyperkalaemia, the incidence of which is increased by the co-prevalence of chronic kidney disease (CKD). Management of patients in a bespoke Hyperkalaemia Clinic can be advantageous in facilitating optimal use of RAASi. Method A Hyperkalaemia Clinic was opened in July 2019 in this tertiary renal centre within an NHS trust that hosts 4 district hospital heart failure services. Referrals of patients with left ventricular systolic dysfunction whose RAASi could not be optimised because of hyperkalaemia were encouraged from heart failure specialist nurses and cardiologists. Management of the patients incorporated commencement of patiromer at 8.4g daily and increases in RAASi was usually devolved to the referring team. This report describes the activity and short-term outcomes of the first 17 months after opening of the clinic (follow up until 1st January 2021). Results 34 patients with systolic heart failure and problems with RAASi-associated hyperkalaemia were referred to the clinic. Mean age was 74 (range 44-88) years, 28% had stage 3a, 28% 3b and 8% stage 4 CKD. ACE-I or ARB were being used in 73% of patients at referral, 73% were using beta blockers and 50% MRA with loop diuretic use in 70%. At first visit 64% had normokalaemia, and 36% serum potassium 5.4-6.0 mmol/L. During follow-up, 6 (18%) patients discontinued patiromer due to gastrointestinal side effects, 3 no longer required the binder because of decrease in RAASi use and 2 patients died (one each from stroke and sepsis). One patient was switched to an alternative potassium binder. As of 1st January 2021, patiromer was still being administered to 22 (65%) patients, 8 of which had received this for >12 months; all patients remained normokalaemic and none of them required magnesium supplementation. An increase in RAASi therapy had occurred in only 12 (35%) patients. Conclusion Our experience demonstrates the relative simplicity of managing hyperkalaemia via a bespoke clinic in cardio-renal patients. As this was nephrology-led, optimised management was dependent upon the assertive and collaborative involvement of the referring heart failure teams who helped with biochemical monitoring and alteration of RAASi therapy. However, less than half of the patients benefitted from an increase in RAASi therapy after normalisation of serum potassium, and there was definitely scope for improving this component of the care pathway via more direct multi-disciplinary interaction with the heart failure teams.

Modern real world applications are commonly complex, consisting of multiple subsystems that may interact with or depend on each other. Our case-study about wave energy converters (WEC) for the renewable energy industry shows that in such a multi-component system, optimising each individual component cannot yield global optimality for the entire system, owing to the influence of their interactions or the dependence on one another. Moreover, modelling a multi-component problem is rarely easy due to the complexity of the issues, which leads to a desire for existent models on which to base, and against which to test, calculations. Recently, the travelling thief problem (TTP) has attracted significant attention in the Evolutionary Computation community. It is intended to offer a better model for multicomponent systems, where researchers can push forward their understanding of the optimisation of such systems, especially for understanding of the interconnections between the components. The TTP interconnects with two classic NP-hard problems, namely the travelling salesman problem and the 0-1 knapsack problem, via the transportation cost that non-linearly depends on the accumulated weight of items. This non-linear setting introduces additional complexity. We study this nonlinearity through a simplified version of the TTP - the packing while travelling (PWT) problem, which aims to maximise the total reward for a given travelling tour. Our theoretical and experimental investigations demonstrate that the difficulty of a given problem instance is significantly influenced by adjusting a single parameter, the renting rate, which prompted our method of creating relatively hard instances using simple evolutionary algorithms. Our further investigations into the PWT problem yield a dynamic programming (DP) approach that can solve the problem in pseudo polynomial time and a corresponding approximation scheme. The experimental investigations show that the new approaches outperform the state-of-the-art ones. We furthermore propose three exact algorithms for the TTP, based on the DP of the PWT problem. By employing the exact DP for the underlying PWT problem as a subroutine, we create a novel indicator-based hybrid evolutionary approach for a new bi-criteria formulation of the TTP. This hybrid design takes advantage of the DP approach, along with a number of novel indicators and selection mechanisms to achieve better solutions. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018

An approach to the structural health management (SHM) of future aerospace vehicles is presented. Such systems will need to operate robustly and intelligently in very adverse environments, and be capable of self-monitoring (and ultimately, self-repair). Networks of embedded sensors, active elements, and intelligence have been selected to form a prototypical “smart skin” for the aerospace structure, and a methodology based on multi-agent networks developed for the system to implement aspects of SHM by processes of self-organisation. Problems are broken down with the aid of a “response matrix” into one of three different scenarios: critical, sub-critical, and minor damage. From these scenarios, three components are selected, these being: (a) the formation of “impact boundaries” around damage sites, (b) self-assembling “impact networks”, and (c) shape replication. A genetic algorithm exploiting phase transitions in systems dynamics has been developed to evolve localised algorithms for impact boundary formation, addressing component (a). An ant colony optimisation (ACO) algorithm, extended by way of an adaptive dead reckoning scheme (ADRS) and which incorporates a “pause” heuristic, has been developed to address (b). Both impact boundary formation and ACO-ADRS algorithms have been successfully implemented on a “concept demonstrator”, while shape replication algorithms addressing component (c) have been successfully simulated.