Embedding evolutionary strategy in ordinal optimization for hard optimization problems

作者：Horng Shih Cheng; Yang Feng Yi^*; Lin Shieh Shing

来源：Applied Mathematical Modelling, 2012, 36(8): 3753-3763.

DOI：10.1016/j.apm.2011.11.013

• Evolution strategy
• Ordinal optimization
• Radial basis function
• Optimal computing budget allocation
• Stochastic combinational optimization
• Hotel booking limits

This work proposes a method for embedding evolutionary strategy (ES) in ordinal optimization (OO), abbreviated as ESOO, for solving real-time hard optimization problems with time-consuming evaluation of the objective function and a huge discrete solution space. Firstly, an approximate model that is based on a radial basis RBF) network is utilized to evaluate approximately the objective value of a solution. Secondly, ES associated with the approximate model is applied to generate a representative subset from a huge discrete solution space. Finally, the optimal computing budget allocation (OCBA) technique is adopted to select the best solution in the representative subset as the obtained %26quot;good enough%26quot; solution. The proposed method is applied to a hotel booking limits (HBL) problem, which is formulated as a stochastic combinatorial optimization problem with a huge discrete solution space. The good enough booking limits, obtained by the proposed method, have promising solution quality, and the computational efficiency of the method makes it suitable for real-time applications. To demonstrate the computational efficiency of the proposed method and the quality of the obtained solution, it is compared with two competing methods - the canonical ES and the genetic algorithm (GA). Test results demonstrate that the proposed approach greatly outperforms the canonical ES and GA.

• 出版日期2012-8