Multi-Objective Two-Dimensional Truss Optimization by using Genetic Algorithm  

Oleh:

Alrasyid, Harun ; Aji, Pujo

Jenis: Article from Journal - ilmiah nasional - terakreditasi DIKTI
Dalam koleksi: IPTEK: The Journal for Technology and Science vol. 22 no. 2 (May 2011), page 74-78.
Topik: optimasi truss; algoritma genetika; optimasi multi obyek; truss optimization; genetic algorithm; multi-objective optimization
Fulltext: 62-107-1-SM.pdf (497.96KB)

Isi artikel

During last three decade, many mathematical programming methods have been develop for solving optimization problems. However, no single method has been found to be entirely efficient and robust for the wide range of engineering optimization problems. Most design application in civil engineering involve selecting values for a set of design variables that best describe the behavior and performance of the particular problem while satisfying the requirements and specifications imposed by codes of practice. The introduction of Genetic Algorithm (GA) into the field of structural optimization has opened new avenues for research because they have been successful applied while traditional methods have failed. GAs is efficient and broadly applicable global search procedure based on stochastic approach which relies on “survival of the fittest” strategy. GAs are search algorithms that are based on the concepts of natural selection and natural genetics. On this research Multi-objective sizing and configuration optimization of the two-dimensional truss has been conducted using a genetic algorithm. Some preliminary runs of the GA were conducted to determine the best combinations of GA parameters such as population size and probability of mutation so as to get better scaling for rest of the runs. Comparing the results from sizing and sizing– configuration optimization, can obtained a significant reduction in the weight and deflection. Sizing–configuration optimization produces lighter weight and small displacement than sizing optimization. The results were obtained by using a GA with relative ease (computationally) and these results are very competitive compared to those obtained from other methods of truss optimization.

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