Multi-objective Optimization of LARP Parameters using Weighted Sum DE Method

ABSTRACT: This thesis employed the single-objective differential evolution (DE) algorithm to search the multi-objective solutions to obtain lateral controller (LARP) settings for an auto-steered tractor by combining two fitness functions, lateral peak and RMS errors, to a single objective using the weighted-sum-method. Compared to the multi-objective differential algorithm, weighted-sum DE algorithm covered a larger range of the Pareto-front. After modifying DE to set the search space adaptively, the modified method finds better non-dominated solutions than MODE by less number of fitness evaluations. Weighted Sum DE algorithm obtained better non-dominated solutions than MODE algorithm although weighted sum DE uses 20 000 fitness evaluation while MODE used 500 000 evaluations. Keywords: Weighted sum optimization, LARP control, Automatic steering agricultural vehicle. …………………………………………………………………………………………………………………………