A comparison between optimization algorithms applied to offshore crane design using an online crane prototyping tool
Journal article, Peer reviewed
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Original versionAdvances in Intelligent Systems and Computing. 2017, 533 266-276. 10.1007/978-3-319-48308-5_26
Offshore crane design requires the configuration of a large set of design parameters in a manner that meets customers’ demands and operational requirements, which makes it a very tedious, time-consuming and expensive process if it is done manually. The need to reduce the time and cost involved in the design process encourages companies to adopt virtual prototyping in the design and manufacturing process. In this paper, we introduce a server-side crane prototyping tool able to calculate a number of key performance indicators of a specified crane design based on a set of about 120 design parameters. We also present an artificial intelligence client for product optimisation that adopts various optimization algorithms such as the genetic algorithm, particle swarm optimization, and simulated annealing for optimising various design parameters in a manner that achieves the crane’s desired design criteria (e.g., performance and cost specifications). The goal of this paper is to compare the performance of the aforementioned algorithms for offshore crane design in terms of convergence time, accuracy, and their suitability to the problem domain.