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Self-Regulating and Self-Perception Particle Swarm Optimization with Mutation Mechanism

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Abstract

Particle swarm optimization (PSO) is widely used to solve various optimization problems, such as robotics visual perception and intelligent control under uncertainties, due to its simple rules and easy implementation. However, the PSO has premature convergence in the optimization process, which will lead to inaccurate problems such as uncertainties of the control system. To improve PSOs performance, a self-regulating particle swarm optimization with mutation mechanism (SRM-PSO) is proposed in this paper. SRM-PSO combines the mutation mechanism, self-regulation and self-perception strategy. The mutation mechanism is introduced to generate trial particle moving in different directions to maintain population diversity. Self-regulation and self-perception enable particles to be updated in different ways for fast exploration and intelligent exploitation. To validate the effectiveness of the SRM-PSO, experiments are conducted in the CEC2017 test suite. The test results indicate that SRM-PSO outperforms two related variants, and five representative PSO variants. Further, SRM-PSO is applied to several real-world optimization problems, which demonstrates its potential and competitiveness.

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References

  1. Maharana, H.S., Dash, S.K.: Comparative Optimization Analysis of Ramp Rate Constriction Factor Based PSO and Electro Magnetism Based 1 for Economic Load Dispatch in Electric Power System. In: 2019 International Conference on Applied Machine Learning (ICAML), pp. 63–68 (2019)

  2. Pham, T.X., Siarry, P., Oulhadj, H.: Segmentation of mr brain images through hidden markov random field and hybrid metaheuristic algorithm. IEEE Trans. Image Process. 29, 6507–6522 (2020)

    Article  MathSciNet  Google Scholar 

  3. Wang, Y., Chen, Y., Miao, Z.: Obstacle avoidance planning for de-icing robot based on PSO-fuzzy. Control Engineering of China 21(2), 298–302 (2014)

    Google Scholar 

  4. Li, X., Chen, Y., Yu, X.: Parameters Optimization of Mechanical Arm PID Controllers Based on CLPSO Algorithm. In: 2011 6Th International Conference on Computer Science & Education (ICCSE), pp. 56–60 (2011)

  5. Bai, X., Gao, X., Xue, B.: Particle Swarm Optimization Based Two-Stage Feature Selection in Text Mining. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2018)

  6. Ramos-Figueroa, O., Quiroz-Castellanos, M., Mezura-Montes, E., Schtze, O.: Metaheuristics to solve grouping problems: A review and a case study. Swarm Evol. Comput. 100643, 53 (2020)

    Google Scholar 

  7. Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, Q Rev Biol. 69 (1) 126C137 (Mar 1994)

  8. Wu, G., Shen, X., Li, H., Chen, H., Lin, A., Suganthan, P.N.: Ensemble of differential evolution variants. Inf. Sci. 423, 172–186 (2018)

    Article  MathSciNet  Google Scholar 

  9. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  10. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer, adv. Eng. Softw. 69, 46–61 (2007)

    Google Scholar 

  11. Wu, G.: Across neighbourhood search for numerical optimization. Inf. Sci. 329, 597–618 (2016)

    Article  Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, 4, pp. 1942–1948 (1995)

  13. Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. World Congress on Computational Intelligence (Cat. No. 98TH8360), pp. 69–73 (1998)

  14. Shi, Y., Eberhart, R. C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 3, pp. 1945–1950 (1999)

  15. Zheng, Y., Ma, L., Zhang, L., Qian, J.: A unified particle swarm optimization scheme. In: Proceedings of the IEEE International Conference of Computational Methods in Sciences and Engineering, pp. 221–226 (2003)

  16. Gao, Y., Duan, Y.: A New Particle Swarm Optimization Algorithm with Random Inertia Weight and Evolution Strategy. In: 2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007), pp. 199–203 (2007)

  17. Wu, J., He, X., Zhao, W., Wang, R.: Exponential Inertia Weight Particle Swarm Algorithm for Dynamics Optimization of Electromechanical Coupling System. In: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 479–483 (2009)

  18. Han, W., Yang, P., Ren, H., Sun, J.: Comparison Study of Several Kinds of Inertia Weights for PSO. In: 2010 IEEE International Conference on Progress in Informatics and Computing, pp. 280–284 (2010)

  19. Bao, G., Mao, K.: Particle Swarm Optimization Algorithm with Asymmetric Time Varying Acceleration Coefficients. In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2134–2139 (2009)

  20. Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (Feb 2002)

    Article  Google Scholar 

  21. Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 3, pp. 1931–1938 (1999)

  22. Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (Jun 2004)

    Article  Google Scholar 

  23. Liu, Q., Wei, W., Yuan, H., Zhan, Z., Li, Y.: Topology selection for particle swarm optimization, Inf. Sci. 363, 154–173 (2016)

    Google Scholar 

  24. Liang, J., Qin, A., Suganthan, P. N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)

    Article  Google Scholar 

  25. Lynn, N., Suganthan, P.N.: (2015) Heterogeneous Comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11–24 (2015)

    Google Scholar 

  26. Lin, A., Sun, W., Yu, H., Wu, G., Tang, H.: Global genetic learning particle swarm optimization with diversity enhancement by ring topology, Swarm Evol. Comput. 44, 571–583 (2019)

    Google Scholar 

  27. Zhang, H., Yuan, M., Liang, Y., Liao, Q.: A novel particle swarm optimization based on preyCpredator relationship, Appl. Soft. Comput. 68, 202–218 (2018)

    Article  Google Scholar 

  28. Aydilek, I. B., hybrid firefly, A: particle swarm optimization algorithm for computationally expensive numerical problems. Appl. Soft Comput. 66, 232–249 (2018)

    Article  Google Scholar 

  29. Tan, C., Chang, S., Liu, L.: Hierarchical genetic-particle swarm optimization for bistable permanent magnet actuators, Appl. Soft. Comput. 61, 1–7 (2017)

    Article  Google Scholar 

  30. Tanweer, M. R., Suresh, S., Sundararajan, N.: Self regulating particle swarm optimization algorithm, Inf. Sci. 294, 182–202 (2015)

    MathSciNet  MATH  Google Scholar 

  31. Suresh, S., Savitha, R., Sundararajan, N.: A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN. IEEE Trans. Neural Netw. 22(7), 1061–1072 (2011)

    Article  Google Scholar 

  32. Khan, S. U., Yang, S., Wang, L., Liu, L.: A modified particle swarm optimization algorithm for global optimizations of inverse problems. IEEE Trans. Magn. 52(3), 1–4 (2016)

    Article  Google Scholar 

  33. Andrews, P.S.: An Investigation into Mutation Operators for Particle Swarm Optimization 2006 IEEE International Conference on Evolutionary Computation, pp. 1044–1051 (2006)

  34. Awad, N., Ali, M., Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization, Tech Rep. (2016)

  35. Perez-Bellido, A.M., Salcedo-Sanz, S., Ortiz-Garcia, E.G., Portilla-Figueras, A.: A Hybrid Evolutionary Programming Algorithm for Spread Spectrum Radar Polyphase Codes Design. In: Gecco 2007: Genetic and Evolutionary Computation Conference, pp. 682–688 (2007)

  36. Higashi, N., Iba, H.: Particle swarm optimization with Gaussian mutation. In: Proceedings of the 2003 IEEE Swarm Intelligence Symphosium, pp. 72–79 (2003)

  37. Sarangi, A., Samal, S., Sarangi, S. K.: Analysis of Gaussian & Cauchy Mutations in Modified Particle Swarm Optimization Algorithm. In: 2019 5Th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 463–467 (2019)

  38. Tang, J., Zhao, X.: Particle Swarm Optimization with Adaptive Mutation. In: 2009 WASE International Conference on Information Engineering, pp. 234–237 (2009)

  39. Tanweer, M. R., Auditya, R., Suresh, S., Sundararajan, N., Srikanth, N.: Directionally driven self-regulating particle swarm optimization algorithm, Swarm Evol. Comput. 28, 98–116 (2016)

    Google Scholar 

  40. Tanweer, M.R., Suresh, S., Sundararajan, N.: Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems. Inf. Sci. 326, 1–24 (2016)

    Article  Google Scholar 

  41. Dash, P.P., Patra, D.: Mutation based self regulating and self perception particle swarm optimization for efficient object tracking in a video. Measurement 144, 311–327 (2019)

    Article  Google Scholar 

  42. Harrison, K.R., Engelbrecht, A.P., Ombuki-Berman, B.M.: Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm. Swarm Evol. Comput. 41, 20–35 (2018)

    Article  Google Scholar 

  43. Blackwell, T.M., Bentley, P.J.: Dynamic search with charged swarms. In: Proceedings of the 4th annual conference on genetic and evolutionary computation, pp. 19-26 (2002)

  44. Li, C., Yang, S., Nguyen, T.T.: A self-learning particle swarm optimizer for global optimization problems, IEEE Trans. Syst. Man Cybern. b: Cybern. 42 (3) 627C646 (2012)

  45. Salajegheh, F., Salajegheh, E.: PSOG: Enhanced Particle swarm optimization by a unit vector of first and second order gradient directions. Swarm Evol Comput. 46, 28–51 (2019)

    Article  Google Scholar 

  46. Yousri, D., Allam, D., Eteiba, M. B., Suganthan, P. N.: Chaotic heterogeneous comprehensive learning particle swarm optimizer variants for permanent magnet synchronous motor models parameters estimation. Iran. J. Sci. Technol. Trans. Electr. Eng. 44, 1299–1318 (2020)

    Article  Google Scholar 

  47. Das, S., Suganthan, P.N.: Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems, Technical Report, Jadavpur university, Nanyang Technological University, 341–359 (2010)

  48. Dereli, S., Koker, R.: Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem. Complex Intell Syst. 7(3), 1515–1526 (2021)

    Article  Google Scholar 

  49. Dereli, S., Koker, R.: IW-PSO Approach to the inverse kinematics problem solution of a 7-DOF serial robot manipulator. Sigma. J. Eng. Nat. Sci. 36(1), 77–85 (2018)

    Google Scholar 

  50. Tang, H., Sun, W., Yu, H.: A.Lin, M. xue, Y. Song, A.novel hybrid algorithm based on PSO and FOA for target searching in unknown environments. Appl. Intell. 49(7), 2603–2622 (2019)

    Google Scholar 

  51. Dereli, S., Koker, R.: A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm. Artif. Intell. Rev. 53(2), 949–964 (2020)

    Article  Google Scholar 

  52. Song, B., Wang, Z., Zou, L., An improved, P S O: Algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Appl. Soft Comput. 106960, 100 (2021)

    Google Scholar 

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Project No. 61803089), the Natural Science Foundation of Fujian Province (No. 2019J01213). The authors thank Anping Lin for sharing code.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, Fujian Province, China

    Yanjie Chen, Jinglin Liang, Yangning Wu & Bingwei He

  2. School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian Province, China

    Lixiong Lin

  3. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan Province, China

    Yaonan Wang

  4. National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, 410082, Hunan Province, China

    Yanjie Chen & Yaonan Wang

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  1. Yanjie Chen
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Correspondence to Yanjie Chen.

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Chen, Y., Liang, J., Wu, Y. et al. Self-Regulating and Self-Perception Particle Swarm Optimization with Mutation Mechanism. J Intell Robot Syst 105, 30 (2022). https://doi.org/10.1007/s10846-022-01627-y

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