Hybrid Real-Coded Genetic Algorithm and Variable Neighborhood Search for Optimization of Product Storage


Nindynar Rikatsih, Wayan Firdaus Mahmudy, Syafrial Syafrial


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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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.

DOI: http://dx.doi.org/10.25126/jitecs.201942111