Browsing by Author "Benkhelifa, Elhadj"
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Item Open Access Evolvable Embryonics: 2-in-1 Approach to Self-healing Systems(Elsevier, 2013-07-30) Benkhelifa, Elhadj; Pipe, Anthony; Tiwari, AshutoshThis paper covers the authors’ recent research in the area of evolutionary design optimisation in electronic application domain (Evolvable Hardware). This will be also presented in the context of biologically inspired systems where Evolvable Hardware is concerned with evolutionary synthesis of self-healing systems and potentially hardware capable of online adaptation to dynamically changing environment. We will also illustrate how EAs can produce novel and unintuitive design solutions, and possibly new design principles. The novelty of this research project addresses this compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired mechanism to support the design optimisation of self-healing architectures, that is Evolvable-Embryonics.Item Open Access Micro-electro-mechanical system design synthesis and optimisation by the means of genetic algorithms(Cranfield University, 2010-10) Orlovs, Ilja; Tiwari, Ashutosh; Benkhelifa, ElhadjMicro{Electro{Mechanical Systems (MEMSs) are microscopic (1 to 100 m in size) electro{mechanical systems that are widely used in a variety of commercial applications owing to their small size, energy consumption and, in some cases, high precision. The conventional approaches to design optimisation of MEMS is complex, time consuming and requires a multidisciplinary team with considerable expertise. Hence, exploring the utility of other unconventional paradigms becomes desirable. This thesis focuses on the MEMS design synthesis and optimisation by means of evolutionary algorithms. Con/d.Item Open Access A multi-objective and multidisciplinary optimisation algorithm for microelectromechanical systems(2017-09-14) Farnsworth, Michael; Tiwari, Ashutosh; Zhu, Meiling; Benkhelifa, ElhadjMicroelectromechanical systems (MEMS) are a highly multidisciplinary field and this has large implications on their applications and design. Designers are often faced with the task of balancing the modelling, simulation and optimisation that each discipline brings in order to bring about a complete whole system. In order to aid designers, strategies for navigating this multidisciplinary environment are essential, particularly when it comes to automating design synthesis and optimisation. This paper outlines a new multi-objective and multidisciplinary strategy for the application of engineering design problems. It employs a population-based evolutionary approach that looks to overcome the limitations of past work by using a non-hierarchical architecture that allows for interaction across all disciplines during optimisation. Two case studies are presented, the first focusing on a common speed reducer design problem found throughout the literature used to validate the methodology and a more complex example of design optimisation, that of a MEMS bandpass filter. Results show good agreement in terms of performance with past multi-objective multidisciplinary design optimisation methods with respect to the first speed reducer case study, and improved performance for the design of the MEMS bandpass filter case study.