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Species-Based Differential Evolution with Migration for Multimodal Optimization

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

Abstract

Due to the fact that many realistic problems are multimodal optimization problems (MMOPs), multimodal optimization has attracted a growing interest. In this paper, a Species-based differential evolution with migration algorithm is proposed for MMOPs. First, a migration strategy is developed to improve the exploration ability of species with less individuals. Then, an improved operation of creating local individuals is introduced to help the algorithm to find high-quality solutions. Finally, an archive mechanism is applied to preserve the best solutions found during previous generations and avoid the loss of the peaks. Experimental results on CEC2013 test problems confirm the effectiveness of the proposed algorithm compared to several well-known multimodal optimization algorithms.

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References

  1. Yoo, C.H.: A new multi-modal optimization approach and its application to the design of electric machines. IEEE Trans. Magn. 54(3), 1–4 (2018)

    Article  Google Scholar 

  2. Tanabe, R., Ishibuchi., H.: A review of evolutionary multi-modal multi-objective optimization. IEEE Trans. Evol. Comput., 1–9 (2019). https://doi.org/10.1109/TEVC.2019.2909744

  3. Peng, X.G., Jin, Y.C., Wang, H.: Multimodal optimization enhanced cooperative co-evolution for large-scale optimization. IEEE Trans. Cybern. 49(9), 3507–3520 (2019)

    Article  Google Scholar 

  4. Naik, A., Satapathy, S.C., Ashour, A.S., Dey, N.: Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Comput. Appl. 30, 271–287 (2016). https://doi.org/10.1007/s00521-016-2686-9

    Article  Google Scholar 

  5. Yazdanpanah, A., Singh, R., Gole, A., Filizadeh, S., Muller, J.C., Jayasinghe, R.P.: A parallel multi-modal optimization algorithm for simulation-based design of power systems. IEEE Trans. Power Delivery 30(5), 2128–2137 (2015)

    Article  Google Scholar 

  6. Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: IEEE Congress on Evolutionary Computation, 19–23 June, Portland, OR, USA, pp. 1382–1389 (2004)

    Google Scholar 

  7. Li, J.-P., Balazs, M.E., Parks, G.T., Clarkson, P.J.: A species conserving genetic algorithm for multimodal function optimization. Evol. Comput. 10(3), 207–234 (2002)

    Article  Google Scholar 

  8. Dong, W., Zhou, M.: Gaussian classifier-based evolutionary strategy for multimodal opti-mization. IEEE Trans. Neural Netw. Learn. Syst. 25(6), 1200–1216 (2014)

    Article  Google Scholar 

  9. Petrowski, A.: A clearing procedure as a niching method for genetic algorithms. In: IEEE Congress on Evolutionary Computation, 20–22 May, Nagoya, Japan, pp. 798–803 (1996)

    Google Scholar 

  10. Goldberg, D.-E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: International Conference on Genetic Algorithms, pp. 41–49 (1987)

    Google Scholar 

  11. Storn, R., Price, K.V.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  12. Li, X.: Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans. Evol. Comput. 14(1), 150–169 (2010)

    Article  Google Scholar 

  13. Yang, Q., Chen, W.-N., Yu, Z., Li, Y., Zhang, H., Zhang, J.: Adaptive multimodal continuous ant colony optimization. IEEE Trans. Evol. Comput. 21(2), 191–205 (2017)

    Article  Google Scholar 

  14. Biswas, S., Kundu, S., Das, S.: Inducing niching behavior in differential evolution through local information sharing. IEEE Trans. Evol. Comput. 19(2), 246–263 (2015)

    Article  Google Scholar 

  15. Hui, S., Suganthan, P.-N.: Ensemble and arithmetic recombination based speciation differen-tial evolution for multimodal optimization. IEEE Trans. Cybern. 46(1), 64–74 (2016)

    Article  Google Scholar 

  16. Biswas, S., Kundu, S., Das, S.: An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution. IEEE Trans. Cybern. 44(10), 1726–1737 (2014)

    Article  Google Scholar 

  17. Li, X.: Efficient differential evolution using speciation for multimodal function optimization. In: Conference on Genetic & Evolutionary Computation, pp. 873–880. ACM (2005)

    Google Scholar 

  18. Li, X.: Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3102, pp. 105–116. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24854-5_10

    Chapter  Google Scholar 

  19. Li, J.P., Balazs, M.E., Parks, G.T., Clarkson, P.-J.: Erratum: a species conserving genetic algorithm for multimodal function optimization. Evol. Comput. 11(1), 107–109 (2003)

    Article  Google Scholar 

  20. Li, X., Engelbrecht, A., Epitropakis, M.: Benchmark functions for CEC 2013 special session and competition on niching methods for multimodal function optimization. Technical Report, Royal Melbourne Institute of Technology (2013)

    Google Scholar 

  21. Li, X.: A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio. In: Genetic and Evolutionary Computation Conference (GECCO 2007), 7–11 July, pp. 78–85. ACM, London (2007)

    Google Scholar 

  22. Qu, B.Y., Suganthan, P.N., Liang, J.J.: Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans. Evol. Comput. 16(5), 601–614 (2012)

    Article  Google Scholar 

  23. Wang, Y., Cai, Z.X., Zhang, Q.F.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15, 55–66 (2011)

    Article  Google Scholar 

  24. Alcal-Fdez, J., et al.: KEEL: a software tool to as-sess evolutionary algorithms to data mining problems. Soft. Comput. 13(3), 307–318 (2009)

    Article  Google Scholar 

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Acknowledgements

This research is partly supported by the Doctoral Foundation of Xi’an University of Technology (112-451116017), National Natural Science Foundation of China under Project Code (61803301, 61773314).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Wei Li, Yaochi Fan & Qiaoyong Jiang

  2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an, 710048, China

    Wei Li

Authors
  1. Wei Li
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  2. Yaochi Fan
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  3. Qiaoyong Jiang
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Corresponding author

Correspondence to Wei Li .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Li, W., Fan, Y., Jiang, Q. (2020). Species-Based Differential Evolution with Migration for Multimodal Optimization. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_17

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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