HKn-RVEA: a novel many-objective evolutionary algorithm for car side impact bar crashworthiness problem
by Gaurav Dhiman; Amandeep Kaur
International Journal of Vehicle Design (IJVD), Vol. 80, No. 2/3/4, 2019

Abstract: In this paper, a novel hybrid many-objective evolutionary algorithm, named as hypervolume indicator based on knee point driven and reference vector guided evolutionary algorithm (HKn-RVEA) is proposed. HKn-RVEA is based on the hypervolume indicator, knee points, and reference vector adaptation strategies. The knee points are used to improve the search ability. The reference vectors are used to decompose the optimisation problem into a number of sub-problems. In the proposed algorithm, an adaptation strategy is used to adjust the distribution of the knee points and reference vectors. The proposed algorithm is compared with five well-known evolutionary algorithms over standard benchmark test functions. The results show the performance of HKn-RVEA is better than the competitor algorithms in terms of inverted generational distance (IGD) and hypervolume (HV ) performance measures. HKn-RVEA is also applied to real-life car side crashworthiness problem to demonstrate its efficiency. The experimental results show that the proposed algorithm is able to solve many-objective real-life problems.

Online publication date: Mon, 28-Sep-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Design (IJVD):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com