A Comparison Between Optimization Algorithms Applied to Offshore Crane Design Using an Online Crane Prototyping Tool

Hameed, Ibrahim A.; Bye, Robin Trulssen; Osen, Ottar L.

doi:10.1007/978-3-319-48308-5_26

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…The performance of GWO algorithm in terms of fitness value and convergence time compared to that of very well known optimization algorithms in literature such as GA, PSO, and SA for the same benchmark problem applied to the objective function given by Eq. (1) [8] is shown in Table III. Results from Table III show that the proposed GWO algorithm outperforms other algorithms in terms of stability, achieved fitness value and convergence time.…”

Section: Resultsmentioning

confidence: 99%

“…The GUI also provides a simple visualization of the designed crane and its 2D workspace safe working load (SWL) chart [5]. Additionally, the MCOC uses various optimization algorithms for optimizing the design parameters in a manner that achieves the crane's desired design criteria [7][8]. Each KPI is typically related to overall performance, weight and cost of the designed crane.…”

Section: Offshore Crane Design Software Frameworkmentioning

confidence: 99%

“…This large number of parameters makes the space of all possible combinations of parameter values very large and a manual trial-and-error approach will necessarily be cumbersome and running the risk of suboptimal designs. Previously [7][8], we used the Matlab ® Global Optimization Toolbox from Mathworks ® [11] to implement a crane optimization client (MCOC) that used evolutionary algorithms to find the best configuration of the design parameters. Here, we extend the MCOC with the GWO algorithm.…”

Section: E Matlab Crane Optimisation Client (Mcoc)mentioning

confidence: 99%

“…We have previously developed a computer-automated design (CautoD) software framework for product design optimization and tested it on the design of offshore cranes [5][6][7][8]. The framework has several components.…”

Section: Introductionmentioning

confidence: 99%

“…Complementing the AIPO module, we have also implemented a Matlab ® software module, the Matlab ® crane optimization client (MCOC), that also used a GA for single and multiobjective optimization [7]. Subsequently, we have added other evolutionary algorithms such as Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms to the MCOC [8].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Grey wolf optimizer (GWO) for automated offshore crane design

Hameed

Bye

Osen

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Abstract-In this paper, a new meta-heuristic optimization algorithm called Grey Wolf Optimizer (GWO) is applied to offshore crane design. An offshore crane is a pedestal-mounted elevating and rotating lifting device used to transfer materials or personnel to or from marine vessels, barges and structures whereby the load can be moved horizontally in one or more directions and vertically. Designing and building offshore cranes is a very complex process. It depends on the configuration of a large set of design parameters and is characterized by increased workability and functionality for the owner and cost effectiveness in the total cost of ownership. In an attempt to reduce time and cost involved in the design process, this paper defines a best set of design parameters and uses GWO for the automatic configuration of this set of parameters in a manner that increases the maximum safe working load of the crane and reduces its total weight. Results are verified by a comparative study with other Evolutionary Algorithms (EAs). Results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics.

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Section: Resultsmentioning

confidence: 99%

Section: Offshore Crane Design Software Frameworkmentioning

confidence: 99%

Section: E Matlab Crane Optimisation Client (Mcoc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Grey wolf optimizer (GWO) for automated offshore crane design

Hameed

Bye

Osen

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

Self Cite

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Particle Swarm Optimization Based Approach for Finding Optimal Values of Convolutional Neural Network Parameters

Sinha

Haidar

Verma

2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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An Intelligent Winch Prototyping Tool

Bye¹,

Osen²,

Rekdalsbakken³

et al. 2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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In this paper we present a recently developed intelligent winch prototyping tool for optimising the design of maritime winches, continuing our recent line of work using artificial intelligence for intelligent computer-automated design of offshore cranes. The tool consists of three main components: (i) a winch calculator for determining key performance indicators for a given winch design; (ii) a genetic algorithm that interrogates the winch calculator to optimise a chosen set of design parameters; and (iii) a web graphical user interface connected with (i) and (ii) such that winch designers can use it to manually design new winches or optimise the design by the click of a button. We demonstrate the feasibility of our work by a case study in which we improve the torque profiles of a default winch design by means of optimisation. Extending our generic and modular software framework for intelligent product optimisation, the winch calculator can easily be interfaced to external product optimisation clients by means of the HTTP and WebSocket protocols and a standardised JSON data format. In an accompanying paper submitted concurrently to this conference, we present one such client developed in Matlab that incorporates a variety of intelligent algorithms for the optimisation of maritime winch design.

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A Comparison Between Optimization Algorithms Applied to Offshore Crane Design Using an Online Crane Prototyping Tool

Cited by 4 publications

References 9 publications

Grey wolf optimizer (GWO) for automated offshore crane design

Grey wolf optimizer (GWO) for automated offshore crane design

Particle Swarm Optimization Based Approach for Finding Optimal Values of Convolutional Neural Network Parameters

An Intelligent Winch Prototyping Tool

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