Multi-objective optimization of semi-active suspensions using KEMOGA algorithm

This paper investigates the optimization of semi-active suspension systems operating with various sky-hook (SH) control algorithms. In addition, a novel distribution-based control strategy (CDF) is applied. In contrast to existing works that focus mainly on ride comfort and road holding, in this work we investigate the design of semi-active suspensions with respect to more performance aspects. More specifically, apart from ride comfort and road holding, the trade-off between the dissipated energy and the vibration control performance is considered. Furthermore, the chatter in the response of the vehicle is used as a design criterion. However, in order to consider all these objectives without costing computational time in the optimization procedure, an approach based on KEMOGA algorithm is applied. Firstly, the vehicle model is optimized with respect to ride comfort and road holding using a multi-objective genetic algorithm (MOGA). Each of these two objectives is represented by a single performance index. Then, a sorting algorithm (KE) is applied so as to seek the optimum solution among the alternatives from MOGA considering extra objectives. These extra objectives are introduced in the sorting algorithm (KE) in order to either enhance the two main criteria, being supplementary to them, or because of their importance in the suspension design. Conclusions regarding the optimum design solutions are extracted in addition with the benchmark of them in terms of objectives' values and their design variables. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.