Penerapan Dragonfly Optimization Algorithm (Doa) Pada Permasalahan Multiple Constraints Bounded Knapsack

Abstract

Optimization is very useful in almost all fields in running a business effectively and efficiently to achieve the desired results. This study solves the problem of multiple constraints bounded knapsack by implementing DOA. The problem of multiple constraints bounded knapsack has more than ones constraint with objects that are entered into the storage media, the dimensions can be partially or completely included, but the number of objects is limited. The purpose of this study is to determine the results of using DOA to solve multiple constraits bounded knapsack and the effectiveness of DOA compared to the results of the Simplex method. The data used in this study are primary data. There are ten parameters to be tested, namely population parameters, maximum iteration, s, a, c, f, e and range. The trial results of the ten parameters show that the best value of the parameters is neither too large nor too small. If the best value is too large then the position of the dragonfly will be randomized so that it is not clear the position of the dragonfly and if it is too small the best value then the change is not visible. In addition, based on the results of the final experiment it can be seen that DOA is less effective in solving multiple constraints bounded knapsack problems, because of many experiments there is no solution similar to Simplex. DOA approach to optimal, seen from a small deviation.