HYBRID REAL-CODED GENETIC ALGORITHM AND VARIABLE NEIGHBORHOOD SEARCH FOR OPTIMIZATION OF PRODUCT STORAGE

Rikatsih, Nindynar and Mahmudy, Wayan Firdaus and Syafrial, Syafrial (2019) HYBRID REAL-CODED GENETIC ALGORITHM AND VARIABLE NEIGHBORHOOD SEARCH FOR OPTIMIZATION OF PRODUCT STORAGE. Journal of Information Technology and Computer Science, 4 (2). pp. 166-176. ISSN 2540-9433; E-ISSN 2540-9824

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Abstract

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 (HRCGAVNS) 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.

Item Type: Article
Contributors:
ContributionNameNIDN / NIDKEmail
ReviewerMahmudy, Wayan FirdausNIDN0019097205UNSPECIFIED
ReviewerTolle, HermanNIDN0023087401UNSPECIFIED
Divisions: Informatics Study Program
Depositing User: Yacobus Sudaryono
Date Deposited: 08 Jul 2022 09:06
Last Modified: 04 Sep 2023 03:06
URI: http://repository.itsk-soepraoen.ac.id/id/eprint/711

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