A Systematic Approach for the Selection of Optimization Algorithms including End-User Requirements Applied to Box-Type Boom Crane Design

Abstract

In engineering design, optimization methods are frequently used to improve the initial design of a product. However, the selection of an appropriate method is challenging since many methods exist, especially for the case of simulation-based optimization. This paper proposes a systematic procedure to support this selection process. Building upon quality function deployment, end-user and design use case requirements can be systematically taken into account via a decision matrix. The design and construction of the decision matrix are explained in detail. The proposed procedure is validated by two engineering optimization problems arising within the design of box-type boom cranes. For each problem, the problem statement and the respectively applied optimization methods are explained in detail. The results obtained by optimization validate the use of optimization approaches within the design process. The application of the decision matrix shows the successful incorporation of customer requirements to the algorithm selection.

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

“…The selection of an appropriate optimisation algorithm, approach or tool remains a challenging task, which varies among different end‐users. During the test selection process, some characteristics should be considered [18]: (i) convergence to the optimum value, (ii) low computational time, (iii) ease of use, (iv) the generation of a new design alternative, (v) algorithm customisation, (vi) applicability to the whole product range, (vii) comprehensibility, and (viii) trade‐off decision support. Step 3: Finally, the optimised tests are generated as an output. The core function during optimisation tests is the trade‐off between the size and cost required for validating and executing the test suites.…”

“…The selection of an appropriate optimisation algorithm, approach or tool remains a challenging task, which varies among different end‐users. During the test selection process, some characteristics should be considered [18]: (i) convergence to the optimum value, (ii) low computational time, (iii) ease of use, (iv) the generation of a new design alternative, (v) algorithm customisation, (vi) applicability to the whole product range, (vii) comprehensibility, and (viii) trade‐off decision support.…”