Optimization process for concept design of tactical missiles by using pareto front and TOPSIS

Abstract

Concept design requiring complicated feed-forward and feed-back processes makes it difficult for engineers to determine the global behaviors of design variables and objective functions in a design space. Although design of experiments and response surface models have been applied to overcome these problems, the design variables satisfying the objective functions can’t be found due to violations of given constraints. In this study, a new optimization process, i.e., the GEO (Generate, Explore and Optimize) process for the concept design of a tactical missile, based on a MOGA (Multi-Objective Genetic Algorithm) was proposed, which was first adapted to generate a Pareto Front in order to simultaneously satisfy the constraints and the objective functions. In the first step, the weights between the objective functions were determined by using an AHP (Analytic Hierarchy Process). Then, the design space exploration followed, and was implemented by surrogate models constructed from the Pareto Front with a neural network. In the last step, a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and a desirability function were applied together to determine the optimal solution. The TOPSIS merged the multi-objective design problem to a single entity, and the desirability function normalized each objective function.