Algoritma Ant Colony Optimization pada Quadratic Assignment Problem

Abstract

Quadratic Assignment Problem (QAP) is one extension of the assignment problem by setting n facilities to n certain locations to minimize the total assignment costs. QAP is also a combinatorial optimization problem that is a problem that has a finite set of solutions. Basically the solution of combinatorial problems can be obtained with the right results but for complex problems with larger data sizes it is quite difficult to calculate because the time used is long enough for the completion process. One of the algorithms implemented in the completion of QAP is the Ant Colony Optimization (ACO) algorithm is an algorithm that mimics the behavior of ants in finding food from the nest to a food source with the help of indirect communication called pheromone, so that pheromone is used to find optimal solutions with quite a short time. in this research ACO is used to solve the QAP problem by using a random proportional of rule formula then getting the smallest solution and renewing the pheromone until the assignment is stable and the solution obtained is fixed until the maximum assignment solution. The results obtained to complete the Quadratic Assignment Problem with the Ant Colony Optimization algorithm to get a solution to the QAP problems tested in the Nugent case resulted in a more minimal solution and the placement of appropriate location facilities through pheromone assistance and stored in a taboo list so that all facilities get a decent location with a worth it short time in completion.