A new hybrid real-coded genetic algorithm and its application to parameters identification of soils. Issue 9 (2nd September 2017)

• Record Type: Journal Article
• Authors: Jin, Yin-Fu; Yin, Zhen-Yu; Shen, Shui-Long; Zhang, Dong-Mei
• Is Part Of: Inverse problems in science and engineering. Volume 25:Issue 9(2017)
• Page Start: 1343
• Page End: 1366
• Publication Date: 2017-09-02
• Subjects: RCGA -- optimization -- parameter identification -- soil models -- laboratory and field tests; Engineering mathematics -- Periodicals; Inverse problems (Differential equations) -- Periodicals; 620.001515357
• DOI: 10.1080/17415977.2016.1259315
• Languages: English
• ISSNs: 1741-5977

Abstract

Abstract: Inverse analysis using an optimization method based on a genetic algorithm (GA) is a useful tool for obtaining soil parameters in geotechnical fields. However, the performance of the optimization in identifying soil parameters mainly depends on the search ability of the GA used. This study aims to develop a new efficient hybrid real-coded genetic algorithm (RCGA) being applied to identify parameters of soils. In this new RCGA, a new hybrid strategy is proposed by adopting two crossovers with outstanding ability, namely the Simulated Binary Crossover and the simplex crossover. In order to increase the convergence speed, a chaotic local search technique is used conditionally. The performance of the proposed RCGA is first validated by optimizing mathematical benchmark functions. The results demonstrate that the RCGA has an outstanding search ability and faster convergence speed compared to other hybrid RCGAs. The proposed new hybrid RCGA is then further evaluated by identifying soil parameters based on both laboratory tests and field tests, for different soil models. All the comparisons demonstrate that the proposed RCGA has an excellent performance of inverse analysis in identifying soil parameters, and is thus recommended for use based on all the evaluations carried out in this paper.