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首页> 外文会议>Society of Tribologists and Lubrication Engineers annual meeting amp; exhibition 2009 >APPLICATION OF MODIFIED DIRECT ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION OF AIR BEARINGS
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APPLICATION OF MODIFIED DIRECT ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION OF AIR BEARINGS

机译：改进的直接算法在轴承多目标优化中的应用

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摘要

The goal of a multi-objective optimization is to find the non-dominated solutions which satisfy two or more objectives concurrently. For example, in an aerostatic bearing design a two objectives optimization can be maximizing the bearing stiffness while minimize the air flow, simultaneously. Genetic algorithms (GAs) are usually used to deal with optimization problems with multiple objectives in tribological studies [1-3]. The de facto standard criterion in the search for solutions of non-dominating objectives, satisfying the goal of multi-objective optimization, is Pareto optimality [4]. However, the GAs (revolutionary algorithms) are computationally intensive methods designed for robust global search, but not for efficiency. In a recent single-objective optimization study, a comparison of DIRECT (Dividing RECTangles) algorithm with GA for a global optimization was conducted [5]. In this study of six-factor dynamic system, a reduction of 83% execution time was obtained by DIRECT algorithm as well as with better result. To address the efficiency issue in multi-objective optimization, particle swarm optimization method is used recently to replace the GA in conducting multi-objective optimizations [6]. A significant reduction of computational effort was achieved.
机译：多目标优化的目标是找到同时满足两个或多个目标的非支配解。例如，在空气静力轴承设计中，两个目标的优化可以是最大程度地提高轴承刚度，同时最小化气流。在摩擦学研究中，遗传算法（GA）通常用于处理具有多个目标的优化问题[1-3]。满足多目标优化目标的非主导目标解决方案中的事实上的标准准则是帕累托最优性[4]。但是，GA（革命性算法）是设计用于健壮的全局搜索而不是为了提高效率的计算密集型方法。在最近的单目标优化研究中，进行了DIRECT（分割RECTangles）算法与GA的全局优化比较[5]。在对六因素动态系统的研究中，DIRECT算法减少了83％的执行时间，并且效果更好。为了解决多目标优化中的效率问题，最近使用粒子群优化方法代替遗传算法进行多目标优化[6]。大大减少了计算量。

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