Robust Estimation of Deformation from Observation Differences Using Some Evolutionary Optimisation Algorithms

In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth’s crust surface.

BUCKLING AND POST-BUCKLING OF ISOTROPIC AND COMPOSITE STIFFENED PANELS: A REVIEW ON OPTIMISATION (2000-2015)

Stiffened panels made out of isotropic or anisotropic materials are being extensively used as structural elements for aircraft, maritime, and other structures. In order to maintain stiffness and strength with light weight, new design techniques must be employed when utilising these materials. Their stability, ultimate strength and loading capacity are the key issues pertaining to these engineering structures which have attracted a number of investigators to undertake in- depth research, either in an academic or actual engineering context. This paper presents a review of the optimisation techniques applied to buckling and post-buckling of stiffened panels. Papers published in the period from 2000 to May 2015 have been taken into consideration. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimisation formulations, optimisation algorithms and models available. Finally a critical discussion, giving some practical advice and pointing out and post-buckling of stiffened panels, is provided.

Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

Environmental and Economic Optimisation of Buildings in Portugal and Hungary

Life cycle assessment (LCA) is a scientific method for evaluating the environmental impact of products. Standards provide a general framework for conducting an LCA study and calculation rules specifically for buildings. The challenge is to design energy-efficient buildings that have a low environmental impact, reasonable costs, and high thermal comfort as these are usually conflicting aspects. Efficient mathematical optimisation algorithms can be applied to such engineering problems. In this paper, a framework for automated optimisation is described, and it is applied to a multi-story residential building case study in two locations, Portugal and Hungary. The objectives are to minimise the life cycle environmental impacts and costs. The results indicate that optimum solutions are found at a higher cost but lower global warming potential for Portugal than for Hungary. Optimum solutions have walls with a thermal transmittance in the intervals of 0.29–0.39 and 0.06–0.19 W/m2K for Portugal and Hungary, respectively. Multi-objective optimisation algorithms can be successfully applied to find solutions with low environmental impact and an eco-efficient thermal envelope.