| dc.description.abstract | Generally, optimization is defined as the process of determining the
minimum or maximum value that depends on the function of the goal, even now
there are many problems regarding optimization. One of them is the problem
regarding the selection of goods to be included in a limited storage medium called
Knapsack problem. Knapsack problems have different types and variations. This
study will solve the problem of bounded knapsack multiple constraints by
implementing the Grey Wolf Optimizer (GWO) algorithm. The problem of
bounded knapsack multiple constraints has more than one subject with the items
that are inserted into the dimension storage media can be partially or completely
inserted, but the number of objects is limited. The aim of this study is to determine
the results of using the Grey Wolf Optimizer (GWO) algorithm for solving the
problem of multiple constraints bounded knapsack and compare the optimal
solutions obtained by the simplex method using the Solver Add-In in Microsoft
Excel. The data used in this study is primary data. There are two parameters to be
tested, namely population parameters and maximum iteration. The test results of
the two parameters show that the population parameters and maximum iterations
have the same effect, where the greater the value of the population parameters
and the maximum iteration, the results obtained are also getting closer to the
optimal value. In addition, based on the results of the final experiment it is known
that the comparison of the results of the GWO algorithm and the simplex method
has a fairly small percentage deviation which indicates that the GWO algorithm
produces results that are close to the optimal value. | en_US |
