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International Conference on Intelligent Computing for Sustainable Energy and Environment

International Conference on Life System Modeling and Simulation

ICSEE 2017, LSMS 2017: Intelligent Computing, Networked Control, and Their Engineering Applications pp 181–190Cite as

Improved Artificial Weed Colonization Based Multi-objective Optimization Algorithm

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 762))

Abstract

Nondominated Neighbor Immune Algorithm (NNIA) is a Representative algorithm for multi-objective problems (MOPs). However, for some test problems, the diversity or convergence of NNIA cannot always keep very well. In order to avoid this phenomenon as well as not to increase the number of function evaluations as far as possible, a modified Invasive Weed Optimization (IWO) operator is introduced into NNIA and we proposed an improved NNIA for MOPs, denoted as NNIAIWO. There are three modifications for basic IWO. Firstly, each parent weed generates two weeds called associated parent weeds which do not join in the evaluation but produce new seeds; Secondly, these new seeds generated by the associated parent weeds distribute obey Cauchy distribution near them; Thirdly an oscillator factor is adopted in the calculation of the standard deviation during the iteration process. Fifteen benchmark problems are used to validate the performance of the proposed algorithm. Experimental results shows that NNIAIWO can obtain improved performance on some test problems, meanwhile the numbers of function evaluation do not increase. And for five complex unconstrained MOPs, namely UF, NNIAIWO also presents a better performance than NNIA.

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References

  1. Fonseca, C.M., Fleming, P.J.: Genetic algorithm for multi-objective optimization: Formulation, discussion and generation. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kaufmann Publishers Inc. (1993)

    Google Scholar 

  2. Srinivas, N., Deb, K.: Multi-objective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGAII. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  4. Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  5. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. In: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100. Springer, Berlin (2002)

    Google Scholar 

  6. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inf. 1, 355–366 (2006)

    Article  Google Scholar 

  7. Gong, M., Jiao, L., Du, H., Bo, L.: Multi-objective immune algorithm with nondominated neighbor-based selection. Evol. Comput. 16(2), 225–255 (2008)

    Article  Google Scholar 

  8. Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: a characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30220-9_22

    Chapter  Google Scholar 

  9. Basak, A., Pal, S., Das, S., Snasel, V.: A modified invasive weed optimization algorithm for time-modulated linear antenna array synthesis. In: IEEE Congress on Evolutionary Computation (CEC 2010), pp. 1–8 (2010)

    Google Scholar 

  10. Zitzler, E., Deb, K., Thiele, L.: Comparison of multi-objective evolutionary algorithm: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  11. Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  12. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), pp. 825–830 (2002)

    Google Scholar 

  13. Zhang, Q., Zhou, A., Zhao, S.Z., Suganthan, P.N. Liu, W., Tiwari, S.: Multi-objective optimization test instances for the CEC 2009 special session and competition. Technical report CES-887, University of Essex and Nanyang Technological University (2009)

    Google Scholar 

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61373111, Grant 61272279, Grant 61672405 and Grant 61203303; in part by the Fundamental Research Funds for the Central University under Grant K50511020014, Grant K5051302084, Grant JBG160229, Grant JB150227 and JBJ160229, and in part by the Provincial National Natural Science Foundation of Shanxi of China under Grant 2014JM8321.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Ruochen Liu, Ruinan Wang & Xiao Wang

  2. School of Computer, Xi’an Shi You University, Xi’an, 710065, China

    Manman He

Authors
  1. Ruochen Liu
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  2. Ruinan Wang
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  3. Manman He
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  4. Xiao Wang
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Corresponding author

Correspondence to Ruochen Liu .

Editor information

Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  2. Shanghai University , Shanghai, China

    Chen Peng

  3. Shanghai University , Shanghai, China

    Dajun Du

  4. Nanjing University of Posts and Telecommunications, Nanjing, China

    Tengfei Zhang

  5. Shanghai University , Shanghai, China

    Min Zheng

  6. Swinburne University of Technology, Melbourne, Victoria, Australia

    Qinglong Han

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© 2017 Springer Nature Singapore Pte Ltd.

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Liu, R., Wang, R., He, M., Wang, X. (2017). Improved Artificial Weed Colonization Based Multi-objective Optimization Algorithm. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_19

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  • DOI: https://doi.org/10.1007/978-981-10-6373-2_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6372-5

  • Online ISBN: 978-981-10-6373-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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