Abstract
How to solve the unbalanced supply of raw tomato is a difficult problem which puzzles ketchup factory for a long period of time. Biogeography-based optimization (BBO) is proposed to solve tomato planting planning problem. Nonlinear mathematical model of tomato planting planning is constructed. Some strategies, such as cosine migration model, disturbing migration operator, mutation operator based on Gaussian distribution, and the reasonable combination of mutation operator of differential evolution algorithm (DE) and chaos technology, were used to improve the exploitation and exploration performance of BBO. The problem of planting planning is transformed into the combination optimization problem. We take a Ketchup factory of Xinjiang in China as an example. The simulation results show that the proposed algorithm can meet the balance between raw tomato material production and Ketchup factory capacity. And the proposed algorithm compares with other intelligent optimization algorithms. It verifies rationality of the presented mathematical model and good astringency and feasibility of the proposed algorithm. Thus, optimal planting planning for tomato planting can be achieved.
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Abbreviations
- BBO:
-
Biogeography-based optimization
- DE:
-
Differential evolution algorithm
- PSO:
-
Particle swarm optimization
- GA:
-
Genetic algorithm
References
Adeyemo J, Otieno F (2010) Differential evolution algorithm for solving multi-objective crop planning model. Agric Water Manag 97(6):848–856
Du D, Simon D, Ergezer M (2009) Biogeography-based optimization combined with evolutionary strategy and immigration refusal. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, San Antonio. IEEE, pp 997–1002. doi:10.1109/ICSMC.2009.5346055
Liu B, Wang L, Jin Y-H (2007) Advances in differential evolution. Control Decis 22(7):721–729
Ma H, Simon D (2010) Biogeography-based optimization with blended migration for constrained optimization problems. In: Proceedings of the 12th Annual Conference on Genetic and evolutionary computation, Portland. ACM, pp 417–418. doi:10.1145/1830483.1830561
Ma H, Li X, Lin S (2009) Analysis of migration rate models for biogeography-based optimization. J Southeast Univ (Nat Sci Ed) 39(Suppl 1):16–21
Ma H, Simon D, Fei M, Xie Z (2013) Variations of biogeography-based optimization and Markov analysis. Inf Sci 220:492–506
Mo H, Xu L (2011) Biogeography migration algorithm for traveling salesman problem. Int J Intell Comput Cybern 4(3):311–330
Panchal VK, Singh P, Kaur N, Kundra H (2009) Biogeography based satellite image classification. Int J Comp Sci Info Secur 6(2):269–274
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713. doi:10.1109/TEVC.2008.919004
Wu B, Lin J, Cui Z (2012) Comparative study of migration operator for biogeography-based optimization. Comput Eng Appl 48(25):61–64
Yin G, Zhang Z, Zhang G, Guo X (2009) Multi-objective controlled drainage model based on particle swarm optimized algorithm. Trans Chinese Soc Agri Eng 25(3):6–9
Acknowledgments
This research is supported by the graduate student innovation project of Xinjiang Uygur Autonomous Region (XJGRI2014039).
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Luo, D., Zhang, Hl. Hybrid Biogeography-Based Optimization for Solving Tomato Planting Planning. Agric Res 3, 313–320 (2014). https://doi.org/10.1007/s40003-014-0132-8
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DOI: https://doi.org/10.1007/s40003-014-0132-8