Medalist learning algorithm for configuration optimization of trusses

Sheng-Xue He, Yun-Ting Cui

 (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

Abstract：Truss configuration optimization, involving discrete sizing variables and continuous layout variables, is crucial for various practical applications. This paper introduces a highly efficient metaheuristic algorithm known as the Medalist Learning Algorithm (MLA) to address this optimization problem. Inspired by the learning behavior observed in group dynamics, the MLA offers a concise implementation procedure with two key operations: identifying medalists and facilitating individual learning. By leveraging the learning efficiency derived from a Logistic function, the MLA strikingly balances exploration and exploitation capacities throughout the learning period. To demonstrate its effectiveness, four classical truss structures with up to 44 design variables under multiple loading conditions are utilized to evaluate the MLA's performance in solving sizing and shape optimization problems of varying scales. Comparative analysis between the MLA's results and previously reported findings establishes its superiority in achieving the final feasible best weight. Statistical evidence further illustrates the MLA's consistent ability to deliver competitive solutions for a wide range of truss configuration optimization problems. The MLA's simplicity in implementation ensures its widespread applicability and potential for practical usage in the field of structural optimization.

Key words: structure optimization; truss structure; medalist learning algorithm; layout optimization

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论文网页：

https://doi.org/10.1016/j.asoc.2023.110889

Applied Soft Computing 中科院2区   top 期刊

Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.

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