A Computational Intelligence Approach to Optimization with Unknown Objective Functions

In many practical engineering design problems, the form of objective function is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective function is obtained by some analysis such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. Usually, these analyses are considerably time consuming to obtain a value of objective function. In order to make the number of analyses as few as possible, we suggest a method by which optimization is performed in parallel with predicting the form of objective function. In this paper, radial basis function networks (RBFN) are employed in predicting the form of objective function, and genetic algorithms (GA) in searching the optimal value of the predicted objective function. The effectiveness of the suggested method will be shown through some numerical examples.

• Type: Conference
• Year: 2001
• Where: ICANN
• Authors: Hirotaka Nakayama, Masao Arakawa, Rie Sasaki
• Keywords: Design Variables, ICANN 2001, Neural Networks, Objective Function, Practical Engineering Design