Multi-objective optimization of air bearings using hypercube-dividing method

Multiobjective design of porous air bearing using group inching fortification method

“…In this study, the externally pressurized air bearing pad (Wang and Cha, 2010) has four square porous pads which are flush with the bearing surface (Fig. 1).…”

“…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).…”

“…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).…”

“…Therefore, the commonly applied roulette-wheel selection of offspring in the single-or multi-objective GA is based on the fitness of the target function or the Pareto ranking of multiple objectives, respectively. Similarly, the selection of the design space to be divided for searching the potential optimum in the DIRECT algorithm (Zhu and Bogy, 2002;Zhu and Bogy, 2004;Jones, 2001) or HDM (Wang and Cha, 2010) is based on the merit of the objective function or the Pareto ranking, respectively. The difference among the population-based multiobjective optimization methods is that the strategy of generating new designs (points) in the subsequent epochs or generations.…”

“…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.…”