Application of the Genetic Algorithm to the Multi-Objective Optimization of Air Bearings

Wang, Nenzi; Chang, Yau-Zen

doi:10.1023/b:tril.0000032436.09396.d4

Cited by 31 publications

(21 citation statements)

References 10 publications

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“…If population size is assumed to be pop, the next generation population generated by selection operation also contains pop individuals. Roulette wheel selection is commonly used to select outstanding individuals from a population (Wang, Chang 2004). Individuals with higher fitness are more opportunities to be selected than those with lower fitness.…”

Section: Improved Genetic Algorithmmentioning

confidence: 99%

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Wang

Yuan

Sun

2018

Journal of Civil Engineering and Management

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Abstract. Due to more complex structure and increasing prefabrication rate of precast concrete buildings, the assembly order between their constituent components is getting more and more attention. In order to solve the assembly sequence planning and optimization (ASPO) problem in precast concrete buildings, Building Information Modelling (BIM) and Improved Genetic Algorithm (IGA) are organically combined to propose a new method called BIM-IGA-based ASPO method. This method uses BIM for parametric modelling, uses IGA to search for an optimal assembly sequence, and then uses BIM again for visual simulation to further test the assembly sequence. Besides, IGA, which is improved in coding mode, crossover operation and mutation operation, is also used to achieve the dynamic adjustment of assembly sequence in construction process. A full-text example is used to explain the detailed operating principle of BIM-IGA-based ASPO method. The results indicate that the method can effectively find an optimal assembly sequence to reduce the assembly difficulty of a precast concrete building.

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Section: Improved Genetic Algorithmmentioning

confidence: 99%

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Wang

Yuan

Sun

2018

Journal of Civil Engineering and Management

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“…Thus, to effectively solve a problem of two or more objectives is of practical importance. Some studies show the results of recent effort in developing efficient schemes (Wang and Chang, 2004;Hirani and Suh, 2005;Wang, 2005;Bhat and Barrans, 2008;Wang and Cha, 2010;Lu and Xie, 2014) for solving multiobjective optimization problems (MOOPs). To minimize the execution time many of these optimization analyses were carried out by using parallel computing or approximating the computationally intensive function with a surrogate model (Li, et al, 2008;Srirat, et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…A few pioneer works for solving multiobjective optimization problems using evolutionary or genetic algorithms in engineering applications can be found in the literature (e.g., Deb, 2001;Coello Coello, et al, 2013;Knowles, et al, 2008;Branke, et al, 2008;Li, et al, 2008). Some recent MOOP solvers for tribological designs are adopted from modifying single-objective optimization algorithms, such as the multiobjective genetic algorithm (GA) (Wang and Chang, 2004;Hirani and Suh, 2005;Wang, 2005;Bhat and Barrans, 2008;Chiba, et al, 2014) and hyper-cube dividing method (HDM) (Wang and Cha, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…In the GIF method the initial group of designs in Pareto rank 1 is used as the basis to inch up the formation of Pareto front, which is fortified over the search process by uniting superior or non-dominated solutions from base-point exploration moves. In this study, a comparison of the GIF method with GA (Wang and Chang, 2004)) and HDM (Wang and Cha, 2010) for a two-objective optimization is conducted. A stopping criterion suitable for the GIF method in the optimization process is also proposed, which is important for effective search.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective design of porous air bearing using group inching fortification method

Wang

Chen

2015

JAMDSM

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This study presents a new approach, group inching fortification (GIF) method, to deal with multiobjective optimization problems found in various mechanical designs. The GIF method is exemplified by a four-factor porous air bearing design. In the GIF method the initial group of designs in Pareto rank 1 is used as the basis to inch up the formation of Pareto solution set, which is fortified over the search process by uniting superior or non-dominated solutions from base-point exploration moves. In this study, a comparison of the GIF method with genetic algorithm (GA) and hyper-cube dividing method (HDM) for the same air bearing design is presented. The results show that the Pareto solution set obtained by the GIF method has more design selections with a wider coverage (breadth). Equally important, the number of objective-function calls required (179, 736, and 3600 for GIF, HDM, and GA, respectively) in the GIF method is significantly reduced. In this study, the GIF method is suggested to be terminated when all the designs are non-dominated by each other, a criterion which is very difficult to achieve by using the GA and HDM. This study proposes an effective design tool which is easy-to-implement for solving the problems with multiple objectives.

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“…In particular global search techniques like GA's [18][19], simulated annealing [20] and direct algorithm [21,22] have been widely used. However, these procedures may be computational expensive due to a large amount of iterations involved.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of Pareto optimal bearings subject to surface error variations

Bhat

Barrans

Kumar

2010

Tribology International

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A Pareto optimization study was carried out on a flat pad aerostatic bearing design. Some of the Pareto optimal configurations were then subjected to surface profiling errors including tilt, concavity, convexity and waviness and key performance parameters such as load capacity, stiffness and flow rate determined. From these studies it was concluded that multi-orifice aerostatic flat pad bearings are highly sensitive to surface profile variations and these surface profile variations are inherent limitations of the current manufacturing techniques. A technique to account for the sensitivity to manufacturing tolerance within air bearing optimization studies is also proposed.

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Application of the Genetic Algorithm to the Multi-Objective Optimization of Air Bearings

Cited by 31 publications

References 10 publications

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Multiobjective design of porous air bearing using group inching fortification method

Performance analysis of Pareto optimal bearings subject to surface error variations

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