SOM is a type of artificial neural network that is trained by unsupervised competitive learning to create a discretized representation of the input space . 12 × 12 neurons were initialized with random weight vector and an initial input vector (including design variables and objectives) was randomly selected from the Pareto solutions. Each node is then examined to calculated the distance bewteen its weight vector and the input vector and hence the Best Matching Unit (BMU) can be obtained. The different values of design variables and objectives are denoted by the different colors and elements in the same position in the maps resprent a set of design variables and their corresponding objectives on the Pareto front.