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A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm

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Abstract

Multi-objective optimization algorithms have recently attracted much attention as they can solve problems involving two or more conflicting objectives effectively and efficiently. However, most existing studies focus on improving the performance of the solutions in the objective spaces. This paper proposes a novel multimodal multi-objective pigeon-inspired optimization (MMOPIO) algorithm where some mechanisms are designed for the distribution of the solutions in the decision spaces. First, MMOPIO employs an improved pigeon-inspired optimization (PIO) based on consolidation parameters for simplifying the structure of the standard PIO. Second, the self-organizing map (SOM) is combined with the improved PIO for better control of the decision spaces, and thus, contributes to building a good neighborhood relation for the improved PIO. Finally, the elite learning strategy and the special crowding distance calculation mechanisms are used to prevent premature convergence and obtain solutions with uniform distribution, respectively. We evaluate the performance of the proposed MMOPIO in comparison to five state-of-the-art multi-objective optimization algorithms on some test instances, and demonstrate the superiority of MMOPIO in solving multimodal multi-objective optimization problems.

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References

  1. Ali M Z, Awad N H, Duwairi R M. Multi-objective differential evolution algorithm with a new improved mutation strategy. Int J Artif Intell, 2016, 14: 23–41

    Google Scholar 

  2. Gong D M, Qin N N, Sun X Y. Evolutionary algorithms for optimization problems with uncertainties and hybrid indices. Inf Sci, 2011, 181: 4124–4138

    Article  Google Scholar 

  3. Guan X M, Zhang X J, Lv R L, et al. A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation. Sci China Inf Sci, 2017, 60: 112202

    Article  Google Scholar 

  4. Qu B Y, Zhou Q, Xiao J M, et al. Large-scale portfolio optimization using multiobjective evolutionary algorithms and preselection methods. Math Problems Eng, 2017, 2017: 1–14

    Google Scholar 

  5. Tian Y, Cheng R, Zhang X, et al. An indicator-based multiobjective evolutionary algorithm with reference point adaptation for better versatility. IEEE Trans Evol Comput, 2018, 22: 609–622

    Article  Google Scholar 

  6. Liang J J, Zheng B, Qu B Y, et al. Multi-objective differential evolution algorithm based on fast sorting and a novel constraints handling technique. In: Proceedings of IEEE Congress on Evolutionary Computation, Beijing, 2014. 445–450

    Google Scholar 

  7. Gong D W, Sun J, Ji X. Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems. Inf Sci, 2013, 233: 141–161

    Article  MathSciNet  MATH  Google Scholar 

  8. Rong M, Gong D W, Zhang Y, et al. Multidirectional prediction approach for dynamic multiobjective optimization problems. IEEE Trans Cybern, 2018. doi: 10.1109/TCYB.2018.2842158

    Google Scholar 

  9. Zhang X, Zheng X, Cheng R, et al. A competitive mechanism based multi-objective particle swarm optimizer with fast convergence. Inf Sci, 2018, 427: 63–76

    Article  MathSciNet  Google Scholar 

  10. Liu Y P, Gong D W, Sun J, et al. A many-objective evolutionary algorithm using a one-by-one selection strategy. IEEE Trans Cybern, 2017, 47: 2689–2702

    Article  Google Scholar 

  11. Liu Y P, Gong D W, Sun X, et al. Many-objective evolutionary optimization based on reference points. Appl Soft Comput, 2017, 50: 344–355

    Article  Google Scholar 

  12. Gong D W, Sun J, Miao Z. A set-based genetic algorithm for interval many-objective optimization problems. IEEE Trans Evol Comput, 2018, 22: 47–60

    Article  Google Scholar 

  13. Preuss M, Kausch C, Bouvy C, et al. Decision space diversity can be essential for solving multiobjective real-world problems. In: Proceedings of the 19th International Conference on Multiple Criteria Decision Making, Auckland, 2010. 367–377

    MATH  Google Scholar 

  14. Liang J J, Yue C T, Qu B Y. Multimodal multi-objective optimization: a preliminary study. In: Proceedings of IEEE Congress on Evolutionary Computation, Vancouver, 2016. 2454–2461

    Google Scholar 

  15. Liang J J, Qu B Y, Mao X B, et al. Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization. Neurocomputing, 2014, 137: 252–260

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Liang J J, Ma S T, Qu B Y, et al. Strategy adaptative memetic crowding differential evolution for multimodal optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Brisbane, 2012. 1–7

    Google Scholar 

  18. Deb K, Tiwari S. Omni-optimizer: a procedure for single and multi-objective optimization. In: Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, Guanajuato, 2005. 47–61

    MATH  Google Scholar 

  19. Yue C T, Qu B Y, Liang J J. A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Trans Evol Comput, 2018, 22: 805–817

    Article  Google Scholar 

  20. Liang J, Guo Q Q, Yue C T, et al. A self-organizing multi-objective particle swarm optimization algorithm for multimodal multi-objective problems. In: Proceedings of International Conference on Swarm Intelligence, Shanghai, 2018. 550–560

    Google Scholar 

  21. Liang J J, Chan C C, Huang V L, et al. Improving the performance of a FBG sensor network using a novel dynamic multi-swarm particle swarm optimizer. In: Proceedings of SPIE - The International Society for Optical Engineering, Boston, 2005. 373–378

    Google Scholar 

  22. Liang J J, Pan Q K, Chen T J, et al. Solving the blocking flow shop scheduling problem by a dynamic multi-swarm particle swarm optimizer. Int J Adv Manuf Technol, 2011, 55: 755–762

    Article  Google Scholar 

  23. Liang J J, Song H, Qu B Y, et al. Comparison of three different curves used in path planning problems based on particle swarm optimizer. Math Problems Eng, 2014, 2014: 1–15

    Google Scholar 

  24. Yang Q, Chen W N, Yu Z, et al. Adaptive multimodal continuous ant colony optimization. IEEE Trans Evol Comput, 2017, 21: 191–205

    Article  Google Scholar 

  25. Duan H, Qiao P. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int J Intel Comp Cyber, 2014, 7: 24–37

    Article  MathSciNet  Google Scholar 

  26. Xin L, Xian N. Biological object recognition approach using space variant resolution and pigeon-inspired optimization for UAV. Sci China Technol Sci, 2017, 60: 1577–1584

    Article  Google Scholar 

  27. Lei X, Ding Y, Wu F X. Detecting protein complexes from DPINs by density based clustering with Pigeon-Inspired Optimization Algorithm. Sci China Inf Sci, 2016, 59: 070103

    Article  Google Scholar 

  28. Qiu H X, Duan H B. Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design. Sci China Technol Sci, 2015, 58: 1915–1923

    Article  Google Scholar 

  29. Kohonen T. Automatic formation of topological maps of patterns in a self-organizing system. In: Proceedings of the 2nd Scandinavian Conference on Image Analysis, Simula, 1981. 214–220

    Google Scholar 

  30. Liu G, Yang H. Self-organizing network for variable clustering. Ann Oper Res, 2018, 263: 119–140

    Article  MathSciNet  MATH  Google Scholar 

  31. Jin H, Shum W H, Leung K S, et al. Expanding self-organizing map for data visualization and cluster analysis. Inf Sci, 2004, 163: 157–173

    Article  MathSciNet  Google Scholar 

  32. Tsai W P, Huang S P, Cheng S T, et al. A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map. Sci Total Environ, 2017, 579: 474–483

    Article  Google Scholar 

  33. Zhang H, Zhou A, Song S, et al. A self-organizing multiobjective evolutionary algorithm. IEEE Trans Evol Comput, 2016, 20: 792–806

    Article  Google Scholar 

  34. Chen J H, Su M C, Cao R, et al. A self organizing map optimization based image recognition and processing model for bridge crack inspection. Autom Constr, 2017, 73: 58–66

    Article  Google Scholar 

  35. Gu F, Cheung Y M. Self-organizing map-based weight design for decomposition-based many-objective evolutionary algorithm. IEEE Trans Evol Comput, 2018, 22: 211–225

    Article  Google Scholar 

  36. Haykin S S. Neural Networks and Learning Machines. Beijing: China Machine Press, 2009

    Google Scholar 

  37. Rudolph G, Naujoks B, Preuss M. Capabilities of EMOA to detect and preserve equivalent pareto subsets. In: Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, Matsushima, 2007. 36–50

    Google Scholar 

  38. Tang L, Wang X. A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems. IEEE Trans Evol Comput, 2013, 17: 20–45

    Article  Google Scholar 

  39. Zhou A M, Zhang Q F, Jin Y C. Approximating the set of pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm. IEEE Trans Evol Comput, 2009, 13: 1167–1189

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Yi Hu, Jie Wang, Jing Liang, Kunjie Yu, Qianqian Guo, Caitong Yue & Yanli Wang

  2. Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, 450001, China

    Yi Hu

  3. Computer Science and Information Technology, School of Science, RMIT University, Melbourne, VIC, 3000, Australia

    Hui Song

Authors
  1. Yi Hu
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  2. Jie Wang
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  3. Jing Liang
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  4. Kunjie Yu
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  5. Hui Song
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  6. Qianqian Guo
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  7. Caitong Yue
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  8. Yanli Wang
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Correspondence to Jing Liang.

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Hu, Y., Wang, J., Liang, J. et al. A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm. Sci. China Inf. Sci. 62, 70206 (2019). https://doi.org/10.1007/s11432-018-9754-6

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