Dual level searching approach for solving multi-objective optimisation problems using hybrid particle swarm optimisation and bats echolocation-inspired algorithms

A dual level searching approach for multi objective optimisation problems using particle swarm optimisation and modified adaptive bats sonar algorithm is presented. The concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is integrated with the established particle swarm optimisation algorithm. The proposed algorithm incorporates advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handle the complexity of multi objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi objective optimisation benchmark test functions and problem. The acquired results show that the proposed algorithm perform well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving of multi objective optimisation problems

On Quality in Radiotherapy Treatment Plan Optimisation

Radiotherapy is one of the essential treatments used in the fight against cancer. The goal of radiotherapy is to deliver a high dose of ionising radiation to the tumour volume and at the same time minimise the effect on healthy tissue by reducing the radiation to critical organs. This contradiction is challenging and has been driving the research and development of the treatments.Over the last two decades, there has been tremendous technical development inradiotherapy. The rapid increase in computational power introduced treatment plan optimisation and intensity-modulated radiotherapy (IMRT). IMRT made it possible to shape the radiation dose distribution closely around the target volume avoiding critical organs to a greater extent. Rotational implementation of IMRT, e.g. Volumetric Modulated Arc Therapy (VMAT) further improved this “dose shaping” ability. With these techniques increasing the ability to produce better treatment plans, there was a need for evaluation tools to compare the treatment plan quality. A plan can be judged by how well it fulfils the prescription and dose-volume constraints, ideally based on treatment outcome. In this work, this is denoted Required Plan Quality, the minimum quality to accept a plan for clinical treatment. If a plan does not fulfil all the dose-volume constraints, there should be a clear priority of which constraints are crucial to achieve. On the other hand, if the constraints are easily fulfilled, there might be a plan of better quality only limited by the treatment systems ability to find and deliver it. This is denoted Attainable Plan Quality in this work– the quality possible to achieve with a given treatment system for a specific patient group.In work described in this thesis, the so-called Pareto front method was used to search for the attainable plan quality to compare different treatment planning systems and optimisation strategies. More specifically, a fall-back planning system for backup planning and an optimiser to find the best possible beam angles. The Pareto method utilises a set of plans to explore the trade-off between target and nearby risk organs.The Pareto plan generation is time-consuming if done manually. The Pareto method was then used in a software that automated the plan generation allowing for a more accurate representation of the trade-off. The software was used to investigate the attainable plan quality for prostate cancer treatments. In the last two publications in this thesis, machine learning approaches were developed to predict a treatment plancloser to the attainable plan quality compared to a manually generated plan.In the thesis, tools have been developed to help move the treatment plan qualityfrom Required Plan Quality towards the Attainable Plan Quality, i.e. the best quality we can achieve with our current system

Particle swarm optimisation algorithms and their application to controller design for flexible structure systems

Particle swarm optimisation (PSO) is one of the relatively new optimisation techniques, which has become increasingly popular in tuning and designing controllers for different applications. A major problem is that simple PSO have a tendency to converge to local optima, mainly, due to lack of diversity in the particles as the algorithm proceeds and improper selection of other parameters. Maintaining diversity within a population is challenging for PSO, especially for dynamic problems. In order to increase diversity in the search space and to improve convergence, a new variant of PSO is proposed. The increased interest from industry and real-world applications has led to several modifications in the conventional algorithms so as to deal with multiple conflicting objectives and constraints. A modified multi-objective PSO (MOPSO) proposal is made which will allow the algorithm to deal with multi-objective optimisation problems. The main challenge, in designing a MOPSO algorithm, is to select local and global best for each particle so as to obtain a wide range of solutions that trade-off among the conflicting objectives. In the proposed algorithm, a new technique is introduced that combines external archive and non-dominated fronts of the current population in order to select the global best for each particle. The effectiveness of the proposed algorithm is assessed with two examples in controller design for vibration control of flexible structure systems and satisfactory results have been obtained

Multi-objective Optimisation in Additive Manufacturing

Additive Manufacturing (AM) has demonstrated great potential to advance product design and manufacturing, and has showed higher flexibility than conventional manufacturing techniques for the production of small volume, complex and customised components. In an economy focused on the need to develop customised and hi-tech products, there is increasing interest in establishing AM technologies as a more efficient production approach for high value products such as aerospace and biomedical products. Nevertheless, the use of AM processes, for even small to medium volume production faces a number of issues in the current state of the technology. AM production is normally used for making parts with complex geometry which implicates the assessment of numerous processing options or choices; the wrong choice of process parameters can result in poor surface quality, onerous manufacturing time and energy waste, and thus increased production costs and resources. A few commonly used AM processes require the presence of cellular support structures for the production of overhanging parts. Depending on the object complexity their removal can be impossible or very time (and resources) consuming. Currently, there is a lack of tools to advise the AM operator on the optimal choice of process parameters. This prevents the diffusion of AM as an efficient production process for enterprises, and as affordable access to democratic product development for individual users. Research in literature has focused mainly on the optimisation of single criteria for AM production. An integrated predictive modelling and optimisation technique has not yet been well established for identifying an efficient process set up for complicated products which often involve critical building requirements. For instance, there are no robust methods for the optimal design of complex cellular support structures, and most of the software commercially available today does not provide adequate guidance on how to optimally orientate the part into the machine bed, or which particular combination of cellular structures need to be used as support. The choice of wrong support and orientation can degenerate into structure collapse during an AM process such as Selective Laser Melting (SLM), due to the high thermal stress in the junctions between fillets of different cells. Another issue of AM production is the limited parts’ surface quality typically generated by the discrete deposition and fusion of material. This research has focused on the formation of surface morphology of AM parts. Analysis of SLM parts showed that roughness measured was different from that predicted through a classic model based on pure geometrical consideration on the stair step profile. Experiments also revealed the presence of partially bonded particles on the surface; an explanation of this phenomenon has been proposed. Results have been integrated into a novel mathematical model for the prediction of surface roughness of SLM parts. The model formulated correctly describes the observed trend of the experimental data, and thus provides an accurate prediction of surface roughness. This thesis aims to deliver an effective computational methodology for the multi- objective optimisation of the main building conditions that affect process efficiency of AM production. For this purpose, mathematical models have been formulated for the determination of parts’ surface quality, manufacturing time and energy consumption, and for the design of optimal cellular support structures. All the predictive models have been used to evaluate multiple performance and costs objectives; all the objectives are typically contrasting; and all greatly affected by the part’s build orientation. A multi-objective optimisation technique has been developed to visualise and identify optimal trade-offs between all the contrastive objectives for the most efficient AM production. Hence, this thesis has delivered a decision support system to assist the operator in the "process planning" stage, in order to achieve optimal efficiency and sustainability in AM production through maximum material, time and energy savings.EADS Airbus, Great Western Researc

Multidisciplinary optimisation of an Unmanned Aerial Vehicle with a fuel cell powered energy system

ALF/ENGAER 139425-J Bernardo Miguel Teixeira Alves. Examination Committee: Chairperson: COR/ENGAER Luís António Monteiro Pessanha; Supervisors: Prof. André Calado Marta, MAJ/ENGAER Luís Filipe da Silva Félix; Member of the Committee: Prof. Pedro Vieira GamboaPara explorar a utilização de células de combustível a hidrogénio como alternativa viável aos combustíveis nocivos em veículos aéreos não-tripulados, um conceito de UAV de classe I foi desenvolvido no Centro de Investigação da Força Aérea (CIAFA). Este trabalho foca-se nos estudos trade-off realizados durante a sua conceção e na subsequente otimização. Primeiro, uma abordagem de otimização multi-objetivo foi utilizada com o auxílio do algoritmo genético NSGA-II para balancear dois objetivos em conflito: peso reduzido; e elevada autonomia. Conclui-se que é possível voar mais de três horas com um peso máximo à descolagem de 21,6 kg, uma célula de hidrogénio de 800 W e 148 g de hidrogénio. Uma configuração mais pesada com maior potência nominal e mais combustível foi descartada devido a um constragimento na envergadura. Posteriormente, com um conceito que satisfaz os requisitos impostos, uma abordagem multi-disciplinar (MDO) foi utilizada para maximizar a autonomia. O software utilizado foi o OpenAeroStruct, método dos elementos finitos (FEM) e o método da malha de vórtices (VLM) para modelar superfícies sustentadoras. Inicialmente, uma condição de cruzeiro e de carga foram utilizadas com torção geométrica da asa como variável de projeto. Posteriormente, maior complexidade foi introduzida atrav´es da utilização de afilamento, corda e envergadura. Finalmente, uma terceira condição de voo foi introduzida com o intuito de garantir o requisito de perda. Com a utilização de MDO foi possível aumentar a autonomia em 21% satisfazendo todos os requisitos. Este trabalho marca um passo importante no desenvolvimento de um futuro protótipo no Centro de Investigação.To explore the use of hydrogen fuel cells as a feasible alternative to pollutant fuels on Unmanned Aerial Vehicles (UAVs), a class I concept was designed at the Portuguese Air Force Research Centre. This work focuses on the trade-off studies performed during its design and on the optimisation that followed. First, a multi-objective optimisation approach was used with the aid of the Algorithm NSGAII to balance between two conflicting objectives: low weight and high endurance. It was found that it is possible to fly for more than 3 hours with a Maximum Take-off Weight of 21.6 kg, an 800 W fuel cell and 148 g of hydrogen. A heavier configuration with more power and fuel was discarded due to a wingspan constraint. Later, after the concept satisfied the project requirements, Multi-Disciplinary Design Optimisation (MDO) was performed to achieve the maximum endurance possible. The software used was OpenAeroStruct, low fidelity Finite Element Analysis (FEA) and Vortex Lattice Method (VLM) to model lifting surfaces. Initially, a cruise and a load flight point were used with wing geometric twist only as design variable. After, more complexity was added by introducing taper, wing chord and span. Finally, a third flight point was introduced to ensure the stall requirements were satisfied. The use of MDO allowed a 21% increase in endurance with a smaller wing area. Other improvements could not be achieved without violation of the constraints. This work marks an important milestone in the development of a future prototype at the Research Centre.N/

Numerical product design: Springback prediction, compensation and optimization

Numerical simulations are being deployed widely for product design. However, the accuracy of the numerical tools is not yet always sufficiently accurate and reliable. This article focuses on the current state and recent developments in different stages of product design: springback prediction, springback compensation and optimization by finite element (FE) analysis. To improve the springback prediction by FE analysis, guidelines regarding the mesh discretization are provided and a new through-thickness integration scheme for shell elements is launched. In the next stage of virtual product design the product is compensated for springback. Currently, deformations due to springback are manually compensated in the industry. Here, a procedure to automatically compensate the tool geometry, including the CAD description, is presented and it is successfully applied to an industrial automotive part. The last stage in virtual product design comprises optimization. This article presents an optimization scheme which is capable of designing optimal and robust metal forming processes efficiently

An Improved NSGA-II and its Application for Reconfigurable Pixel Antenna Design

Based on the elitist non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization problems, an improved scheme with self-adaptive crossover and mutation operators is proposed to obtain good optimization performance in this paper. The performance of the improved NSGA-II is demonstrated with a set of test functions and metrics taken from the standard literature on multi-objective optimization. Combined with the HFSS solver, one pixel antenna with reconfigurable radiation patterns, which can steer its beam into six different directions (θDOA = ± 15°, ± 30°, ± 50°) with a 5 % overlapping impedance bandwidth (S11 < − 10 dB) and a realized gain over 6 dB, is designed by the proposed self-adaptive NSGA-II