Multiobjective Optimization of a Vehicle Vibration Model Using the Improved Compressed-Objective Genetic Algorithm with Convergence Detection

Abstract

Ride quality and road holding capacity of a vehicle is significantly influenced by its suspension system. In the design process, a number of objective functions related to comfort and road holding capacity are taken into consideration. In this paper, the five-degree-of-freedom system of vehicle vibration model with passive suspension is investigated. This multiobjective optimization problem consists of five objective functions. Based on these five design objectives, this paper formulates four two-objective optimization problems by considering four pairs of design objectives and one five-objective optimization problem. This paper proposes the use of the improved compressed objective genetic algorithm (COGA-II) with convergence detection. COGA-II is intentionally designed for dealing with a problem having many optimized objectives. Furthermore, the performance of COGA-II was benchmarked with the multiobjective uniform-diversity genetic algorithm (MUGA) utilized in the previous study. From the simulation results, with equal population sizes, COGA-II employing the convergence detection for searching termination uses less numbers of generations for most sets of design objectives than MUGA whose termination condition is defined by the constant maximum number of generations. Moreover, the solutions obtained from COGA-II are obviously superior to those obtained from MUGA regardless of sets of design objective.