Hybrid Real-Coded Genetic Algorithm and Variable Neighborhood Search for Optimization of Product Storage
DOI:
https://doi.org/10.25126/jitecs.201942111Abstract
Agricultural product storage has a problem that need to be noticedbecause 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.
References
P. . Chu and J. . Beasley, “A Genetic Algorithm for the Multidimensional Knapsack Problem,†J. Heuristics, vol. 86, no. 4, pp. 63–86, 1998.
J. Puchinger, G. R. Raidl, and U. Pferschy, “The Core Concept for the Multidimensional Knapsack Problem,†Eur. Conf. Evol. Comput. Comb. Optim., pp. 195–208, 2006.
M. Gupta, “A Fast and Efficient Genetic Algorithm to Solve 0-1 Knapsack Problem,†Int. J. Digit. Appl. Contemp. Res., vol. 1, no. 6, 2013.
V. Yadav and S. Singh, “Genetic Algorithms Based Approach to Solve 0-1 Knapsack Problem Optimization Problem,†Int. J. Innov. Res. Comput. Commun. Eng., pp. 8595–8602, 2016.
W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling,†Adv. Mater. Res., vol. 701, pp. 359–363, 2013.
V. I. Herrera, H. Gaztañaga, A. Milo, A. Saez-de-ibarra, and T. Nieva, “Optimal Energy
Management of a Battery- Supercapacitor based Light Rail Vehicle using Genetic
Algorithms,†IEEE Energy Convers. Congr. Expo., pp. 1359–1366, 2015.
G. Arunkumar, I. Gnanambal, S. Naresh, P. C. Karthik, and J. K. Patra, “ParameterOptimization of Three Phase Boost Inverter Using Genetic Algorithm for Linear Loads,†Energy Procedia, vol. 90, no. December 2015, pp. 559–565, 2016.
Y. Amini, M. B. Gerdroodbary, M. R. Pishvaie, R. Moradi, and S. M. Monfared, “Optimal Control of Batch Cooling Crystallizers by Using Genetic Algorithm,†Case Stud. Therm. Eng., vol. 8, pp. 300–310, 2016.176 JITeCS Volume 4, Number 2, September 2019, pp 166-176 p-ISSN: 2540-9433; e-ISSN: 2540-9824
Y. Yan Song & Guoxing, “A Genetic Algorithm of Test Paper Generation,†no. Iccse, pp.897–901, 2013.
A. Rahmi and W. F. Mahmudy, “Regression Modelling for Precipitation Prediction Using Genetic Algorithms,†TELKOMNIKA, vol. 15, no. 3, pp. 1290–1300, 2017.
W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Hybrid Genetic Algorithms for Part Type Selection and Machine Loading Problems with Alternative Production Plans in Flexible Manufacturing System,†vol. 8, no. 1, pp. 80–93, 2014.
A. Rahmi, W. F. Mahmudy, and S. Anam, “A Crossover in Simulated Annealing for Population Initialization of Genetic Algorithm to Optimize the Distribution Cost,†Accept. to Int. J. Intell. Eng. Syst., vol. 9, no. 2, pp. 177–182, 2016.
C. Science and S. Engineering, “Preventing Premature Convergence in Genetic Algorithm Using DGCA and Elitist Technique,†vol. 4, no. 6, pp. 410–418, 2014.
M. Lozano, F. Herrera, N. Krasnogor, and D. Molina, “Real-Coded Memetic Algorithms with Crossover Hill-Climbing Real-Coded Memetic Algorithms with Crossover HillClimbing,†Evol. Comput., vol. 12, no. 3, pp. 273–302, 2004.
W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Optimization of Part Type Selection and Loading Problem with Alternative Production Plans in Flexible Manufacturing System
using Hybrid Genetic Algorithms-Part 1 : Modelling and Representation,†5th Int. Conf. Knowl. Smart Technol., pp. 75–80, 2013.
W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Optimization of Part Type Selectionand Loading Problem with Alternative Production Plans in Flexible Manufacturing System
using Hybrid Genetic Algorithms – Part 2 : Genetic Operators and Results,†Int. Conf. Knowl. Smart Technol., pp. 81–85, 2013.
N. Mladenovic, D. Urosevic, and D. Perez-Brit, “Variable Neighborhood Search for Minimum Linear Arrangement Problem,†Yugosl. J. Oper. Res. 26, vol. 26, no. 1, pp. 3–16, 2016.
N. Mladenovic and P. Hansen, “Variable Neighborhood Search,†Comput. Oper. Res., vol. 24, no. 11, pp. 1097–1100, 1997.
C. Exp and J. M. Moreno-vega, “Variable Neighbourhood Search for the Quay Crane Scheduling Problem,†pp. 463–468, 2011.
R. A. Aziz, M. Ayob, and Z. Othman, “The Effect of Learning Mechanism in Variables Neighborhood Search,†2012 4th Conf. Data Min. Optim., no. September, pp. 109–113, 2012.
C. Torres-Machi, V. Yepes, J. Alcala, and E. Pellicer, “Optimization of high-performance concrete structures by variable neighborhood search,†Int. J. Civ. Eng., vol. 11, no. 2, 2013.
W. F. Mahmudy, “Optimization of Part Type Selection and Machine Loading Problems in Flexible Manufacturing System Using Variable Neighborhood Search,†Int. J. Comput. Sci.,
P. Hansen and N. Mladenovi, “Variable neighborhood search : Principles and applications c,†Eur. J. Oper. Res., vol. 130, no. 3, pp. 449–467, 2001.
P. Smirnov, M. Melnik, and D. Nasonov, “Performance-aware scheduling of streaming applications using genetic algorithm,†Procedia Comput. Sci., vol. 108, no. June, pp. 2240–2249, 2017.
H. D. Mathias and V. R. Ragusa, “An Empirical Study of Crossover and Mass Extinction in a Genetic Algorithm for Pathfinding in a Continuous Environment,†IEEE Congr. Evol.
Comput., pp. 4111–4118, 2016.
A. H. Beg and M. Z. Islam, “Novel Crossover and Mutation Operation in Genetic Algorithm for Clustering,†IEEE Congr. Evol. Comput., pp. 2114–2121, 2016.
M. Z. Sarwani, A. Rahmi, and W. F. Mahmudy, “An Adaptive Genetic Algorithm for Cost Optimization of Multi-Stage Supply Chain,†J. Telecommun. Electron. Comput. Eng., vol. 9, no. 2, pp. 155–160, 2017.
Downloads
Published
How to Cite
Issue
Section
License
 Creative Common Attribution-ShareAlike 3.0 International (CC BY-SA 3.0)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).