By Indra Dwi Permana Wicaksana
Genetic algorithm is one of methods that can be used to solve complex optimization problems. One of the problems encountered by a fruit company is determine the distribution 5 types of fruits that will be sold to 5 distributors. Genetic algorithm itself is an optimization technique that is based on the evolution of living things, which in the evolution of living beings experience natural selection mechanism (including crossover and mutation) to be able to survive. In addition to the main purpose of implement genetic algorithms in this problem, the study will also try to compare with other optimization methods such as simulated annealing and Firefly Algorithm.
This study also did the design and manufacture of software applications using genetic algorithms for optimization calculations provision and the distribution 5 types of fruits to 5 distributors, so it will get maximum income.
However, the maximum income does not mean a good result. Because the distribution of fruit to distributors should be fair. Therefore, in this case the maximum income must be accepted by the company and distributors. If income is too high will have an impact on the company and distributor. Conversely, if income is too little will have an impact on the company and distributors.
The results of this study showed that 10 times experiment using some number of generations. Genetic algorithms can generate maximum income is Rp. 136 167 000 from tenth experiment. While Simulated Annealing can generate maximum income is Rp. 141 028 000 from tenth experiment. And the Firefly Algorithm can generate maximum income is Rp. 158 844 500 from second experiment.
Besides that results, the performance of Genetic Algorithm and Firefly Algorithm able to get results fast enough from the simulated annealing. It was because of the GA and FA generate many solutions, while SA is only a single solution. However, the results of the SA is good enough than the GA although it takes large iteration. FA get a very big result. And FA were able get maximum global quickly. However, these results are not suitable for use on this problem.