Skip to main content
SpringerLink
Log in

Conservatism and Adventurism in Particle Swarm Optimization Algorithm

  • Conference paper
  • First Online:
Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Included in the following conference series:

Abstract

Particle Swarm Optimization (PSO) is a widely used optimization algorithm in industrial and academic fields. In this paper, three improved PSO variants are proposed. The main ideas of them are that a coefficient v is added to control the velocity augment of particles to the new position on different dimension. The first one is under the guidance of conservatism which is an inspiration of Differential Evolution (DE), namely, particles preserve more information from their previous positions and move in a smaller search space. This algorithm shows that particles are possible to escape from the current neighborhood and for promising search area if they take more previous information. The second one is guided by adventurism for better exploration, which means a larger search space to particles. The third one can be considered as a compromise between conservatism and adventurism. This algorithm shows that a balanced cooperation with a little conservative in more adventures will make PSO more competitive. Experimental results show that the proposed strategies of all the three algorithms are effective based on CEC2015 benchmarks. All of them are better than the traditional PSOs and the third improved variant performs better than all the other competitors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: International Symposium on MICRO Machine and Human Science, pp. 39–43. IEEE (2002)

    Google Scholar 

  2. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (2002)

    Google Scholar 

  3. Storn, R., Price, K.: Differential Evolution a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Kluwer Academic Publishers, Dordrecht (1997)

    MATH  Google Scholar 

  4. Wang, G.G., Hossein Gandomi, A., Yang, X.S., et al.: A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Eng. Comput. 31(7), 1198–1220 (2014)

    Article  Google Scholar 

  5. Du, W.B., Gao, Y., Liu, C., et al.: Adequate is better: particle swarm optimization with limited-information. Appl. Math. Comput. 268, 832–838 (2015)

    MathSciNet  Google Scholar 

  6. Wang, H., Sun, H., Li, C., et al.: Diversity enhanced particle swarm optimization with neighborhood search. Inf. Sci. 223, 119–135 (2013)

    Article  MathSciNet  Google Scholar 

  7. Zhang, L., Tang, Y., Hua, C., et al.: A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Appl. Soft Comput. 28, 138–149 (2015)

    Article  Google Scholar 

  8. Mirjalili, S., Wang, G.G., Coelho, L.S.: Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput. Appl. 25(6), 1423–1435 (2014)

    Article  Google Scholar 

  9. Mahi, M., Baykan, K., Kodaz, H.: A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Appl. Soft Comput. 30, 484–490 (2015)

    Article  Google Scholar 

  10. Delice, Y., Aydoan, E.K., Zcan, U., et al.: A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. J. Intell. Manuf. 28(1), 23–36 (2017)

    Article  Google Scholar 

  11. Thangamani, C., Chidambaram, M.: A novel hybrid genetic algorithm with weighted crossover and modified particle swarm optimization. Artif. Intell. Syst. Mach. Learn. 9(2), 25–30 (2017)

    Google Scholar 

  12. Liang, J.J., Qu, B.Y., Suganthan, P.N., et al.: Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical report 201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China, Nanyang Technological University, Singapore (2014)

    Google Scholar 

  13. Liang, J.J., Qin, A.K., 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 

Download references

Acknowledgments

This research is supported by National Natural Science Foundation of China (61375066, 61374204, 11171040). A thousand thanks should be given to the reviewers for their constructive suggestions and comments, which improved the manuscript greatly.

Author information

Authors and Affiliations

  1. Automation School, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Guangzhi Xu & Rui Li

  2. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Guangzhi Xu, Rui Li & Xinchao Zhao

  3. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xingquan Zuo

Authors
  1. Guangzhi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinchao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingquan Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinchao Zhao .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xu, G., Li, R., Zhao, X., Zuo, X. (2017). Conservatism and Adventurism in Particle Swarm Optimization Algorithm. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_84

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68759-9_84

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation