A TOPOLOGICAL FORMALISM FOR QUANTITATIVE ANALYSIS OF DESIGN SPACES

The objective of this paper is to present a mathematically grounded description of the two topological spaces for the design problem and the design solution. These spaces are derived in a generalized form such that they can be applied by researchers studying engineering design and developing new methods or engineers seeking to understand the influence that changes in the problem space have on the solution space. In addition to the formal definitions of the spaces, including assumptions and limitations, three types of supported reasoning are presented to demonstrate the potential uses. These include similarity analysis to compare spaces, an approach to sensitivity analysis of the solution space to changes in the problem space, and finally a distance measure to determine how far a current proposal is to the feasible solution space. This paper is presented to establish a common vocabulary for researchers when discussing, studying, and supporting the dyadic nature of engineering design (problem-solution co-evolution).

An Energy-segmented Moth-flame Optimization Algorithm for Function Optimization and Performance Measures Analysis

The moth-flame optimization algorithm (MFO) is a novel metaheuristic algorithm for simulating the lateral positioning and navigation mechanism of moths in nature, and it has been successfully applied to various optimization problems. This paper segments the flame energy of MFO by introducing the energy factor from the Harris hawks optimization algorithm, and different updating methods are adopted for moths with different flame-detection abilities to enhance the exploration ability of MFO. A new energy-segmented moth-flame optimization algorithm (ESMFO) is proposed and is applied on 21 benchmark functions and an engineering design problem. The experimental results show that the ESMFO yields very promising results due to its enhanced exploration, exploitation, and convergence capabilities, as well as its effective avoidance of local optima, and achieves better performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.

Development and delivery of a work experience week programme for mechanical engineering

This paper focuses on the development, delivery and preliminary impact analysis of an engineering Work Experience Week (WEW) programme for KS4 students in the School of Civil, Aerospace and Mechanical Engineering (CAME) at the University of Bristol, UK. Key stage 4, is the legal term for the two years of school education which incorporate GCSEs in England, age 15–16. The programme aims to promote the engineering profession among secondary school pupils. During the WEW, participants worked as engineering researchers: working in teams, they had to tackle a challenging engineering design problem. The experience included hands-on activities and the use of state-of-the-art rapid prototyping and advanced testing equipment. The students were supervised by a group of team leaders, a diverse group of undergraduate and postgraduate engineering students, technical staff, and academics at the School of CAME. The vision of the WEW programme is to transmit the message that everybody can be an engineer, that there are plenty of different routes into engineering that can be taken depending on pupils’ strengths and interests and that there are a vast amount of different engineering careers and challenges to be tackled by the engineers of the future. Feedback from the participants in the scheme has been overwhelmingly positive.

An Improvement of Probabilistic Feasible Region Method for Reliablity-Based Design Optimization

The decoupled methods for reliability-based design optimization (RBDO) problems are efficient and accurate. Sequential optimization and reliability analysis (SORA) method and probabilistic feasible region (PFR) approach are typical decoupled methods. When there are multiple constraints in RBDO problem, PFR method improves the efficiency of solving this problem by establishing the PFR to reduce the number of unnecessary reliability analysis loops. If the constraint boundary is not fixed in RBDO problem, PFR method may fail to solve and give the wrong result. Based on PFR method, this paper proposed an improvement of PFR method to solve the unfixed constraint boundary problems. The improvement of PFR method may not be efficient as the PFR method in solving the common RBDO problems. But, the improvement of PFR method can solve the RBDO problem with unfixed constraint boundary and has better adaptability. Three applications, a nonlinear mathematical problem, a highly nonlinear mathematical problem and an engineering design problem, are presented to illustrate the accuracy of the improvement of PFR method.

TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem

A common technique used to solve multi-objective optimization problems consists of first generating the set of all Pareto-optimal solutions and then ranking and/or choosing the most interesting solution for a human decision maker (DM). Sometimes this technique is referred to as generate first–choose later. In this context, this paper proposes a two-stage methodology: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM. The novelty of this paper lies in the fact that it is not necessary to know the ideal and nadir solutions of the problem in the TOPSIS method in order to determine the ranking of solutions. To show the utility of the proposed methodology, several original experiments and comparisons between different recognized MOEAs were carried out on a welded beam engineering design benchmark problem. The problem was solved with two and three objectives and it is characterized by a lack of knowledge about ideal and nadir values.