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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Research Article

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

Author(s): Bote Lv, Juan Chen*, Boyan Liu and Cuiying Dong

Volume 19, Issue 2, 2019

Page: [139 - 145] Pages: 7

DOI: 10.2174/1568026619666181130140709

Price: $65

Abstract

Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances.

Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively.

Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate.

Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.

Keywords: AO-BBO algorithm, Support vector machine (SVM), Parameter optimization, Soft-sensing model, Plant medicine extraction, Extraction rate.

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[1]
Dan, S.D. Biogeography-based optimization. IEEE Trans. Evol. Comput., 2008, 12(6), 702-713.
[http://dx.doi.org/10.1109/TEVC.2008.919004]
[2]
Wang, Q.; Chen, J.; Li, Q.S.; Liu, J.C. PID parameter optimization based on improved biogeography-based optimization algorithm. J. Nanjing Univ. Sci. Tech., 2017, 41(4), 519-525.
[3]
Guo, W.; Wang, L.; Wu, Q. An analysis of the migration rates for biogeography-based optimization. Inf. Sci., 2014, 254(19), 111-140.
[http://dx.doi.org/10.1016/j.ins.2013.07.018]
[4]
Mo, H.W.; Xu, L.F. Research of biogeography particle swarm optimization for robot path planning. Neurocomputing, 2015, 148(148), 91-99.
[http://dx.doi.org/10.1016/j.neucom.2012.07.060]
[5]
Gong, W.Y.; Cai, Z.H.; Ling, C.X.; Li, H. A real-coded biogeography-based optimization with mutation. Appl. Math. Comput., 2010, 216(9), 2749-2758.
[http://dx.doi.org/10.1016/j.amc.2010.03.123]
[6]
Ma, H.P. An analysis of the equilibrium of migration models for biogeography-based optimization. Inf. Sci., 2010, 180(18), 3444-3464.
[http://dx.doi.org/10.1016/j.ins.2010.05.035]
[7]
Xu, Z.D.; Mo, H.W. Improvement for migration operator in biogeography-based optimization algorithm. Int. J. Pattern Recognit. Artif. Intell., 2012, 25(3), 544-549.
[8]
Chen, D.; Gong, Q.; Hui, Q.; Zhao, J. Multi-objective generation dispatching for wind power integrated system adopting improved biogeography-based optimization algorithm. Proc. CSEE, 2012, 32(31), 150-158.
[9]
Lu, Y.M.; Wang, Y.; Liu, J.; Wu, L. Improved biogeography-based optimization algorithm. CPEN, 2016, 52(17), 146-151.
[10]
Wang, X.; Duan, H. A hybrid biogeography-based optimization algorithm for job shop scheduling problem. Comput. Ind. Eng., 2014, 73, 96-114.
[http://dx.doi.org/10.1016/j.cie.2014.04.006]
[11]
Tizhoosh, H.R. Opposition-Based Learning: A New Scheme for Machine Intelligence, International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce. Vienna, Austria. 2005, pp. 695-701.
[12]
Xu, Q.Z.; Wang, L.; Wang, N.; Zhao, L. A review of opposition-based learning from 2005 to 2012. Eng. Appl. Artif. Intell., 2014, 29(1), 1-12.
[http://dx.doi.org/10.1016/j.engappai.2013.12.004]
[13]
Ergezer, M.; Simon, D.; Du, D. Oppositional biogeography-based optimization. Evolutionary Computation IEEE Transactions, 2014, 47(10), 1009-1014.
[14]
Ergezer, M.; Dan, S. Oppositional biogeography-based optimization for com-binatorial problems, 2011 IEEE Congress of evolutionary computation. New Orleans, USA. 2011, pp. 1496-1503.
[15]
Ergezer, M.; Sikder, I. Survey of oppositional algorithms., 14th international conference on computer and information technology Dhaka, Bangladesh. 2012, pp. 623-628.
[16]
Goudos, S.K.; Deruyck, M.; Plets, D.; Martens, L.; Joseph, W. Application of opposition-based learning concepts in reducing the power consumption in wireless access networks., 23rd international conference on telecommunications. Thessaloniki, Greece.. 2016, pp. 1-5.
[17]
Xue, H.; Han, P. Improved BBO algorithm and its application in PID optimization of thermal system. J. North China Electric Power Univ., 2016, 43(1), 81-85.
[18]
Chen, J.L. Biogeography-Based optimization model based on gaussian mutation. Math. Comput. Simul., 2013, 30(7), 292-279.
[19]
Han, J.; Liu, C. Adaptive fruit fly optimization algorithm based on bacterial migration. Comput. Eng. Sci., 2014, 36(4), 690-696.
[20]
Liu, C.Z.; Han, J.Y. Adaptive fruit fly optimization algorithm based on bacterial migration. Comput. Eng. Sci., 2014, 36(4), 690-696.
[21]
Wang, S.; Ding, L.; Xie, C.; Guo, Z.; Hu, Y.R. A hybrid differential evolution with elite opposition-based learning. J. Wuhan Univ., 2013, 59(2), 111-116.
[22]
Han, S.J.; Ju, Z.; Mao, J.G.; Zhang, W.Y. Fault diagnosis of transformer based on particle swarm optimization-based support vector machine. High Voltage Eng., 2014, 35(3), 509-513.

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