Multiobjective design of a vehicular structure using metamodelling and an efficient genetic algorithm. (11th October 2007) - Fang, Hongbing; Wang, Qian
A new Non-dominated Sorting (NS) algorithm is presented in this paper to improve the efficiency of Genetic Algorithms (GAs) in solving multiobjective optimisation problems. Commonly used NS algorithms typically have a time complexity of O(MN?) to obtain a set of N non-dominated solutions for M objectives. In the proposed NS algorithm, a new dominance tree structure and a divide-and-conquer mechanism are adopted to reduce the number of redundant comparisons in determining non-dominated solutions. The new algorithm is implemented into Non-dominated Sorting Genetic Algorithm (NSGA)-II and shown to have improved overall efficiency of the entire evolution processes. By combining with the metamodelling technique, the new algorithm is successfully used in the multiobjective design of a vehicular structure for safety improvement and weight reduction.