Hybrid Real-Coded Genetic Algorithm and Variable Neighborhood Search for Optimization of Product Storage
Agricultural product storage has a problem that need to be noticed because it has an impact in gaining the profit according to the number of products and the capacity of storage. Inappropriate combination of product causes high expenses and low profit. To solve the problem, we propose genetic algori...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Brawijaya
2019-09-01
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| Series: | JITeCS (Journal of Information Technology and Computer Science) |
| Online Access: | http://jitecs.ub.ac.id/index.php/jitecs/article/view/111 |
| Summary: | Agricultural product storage has a problem that need to be noticed
because it has an impact in gaining the profit according to the number of
products and the capacity of storage. Inappropriate combination of product
causes high expenses and low profit. To solve the problem, we propose genetic
algorithm (GA) as the optimization method. Although GA is good enough to
solve the problem, GA not always gives an optimum result in complex search
spaces because it is easy to be trapped in local optimum. Therefore, we present
a hybrid real-coded genetic algorithm and Variable Neighborhood Search
(HRCGA-VNS) to solve the problem. VNS is applied after reproduction
process of GA to repair the offspring and improve GA exploitation capabilities
in local area to get better result. The test results show that the optimal popsize
of GA is 180, number of generations is 80, combination of cr and mr is 0.7 and
0.3 while optimum Kmax of VNS is 40 with number of iterations 50. Even
though HRCGA-VNS need longer computational time, HRCGA-VNS has
proven to provide a better result based on higher fitness value compared with
classical GA and VNS.
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| ISSN: | 2540-9433 2540-9824 |