SpringerLink
Log in

A Novel Multi-objective Squirrel Search Algorithm: MOSSA

  • Conference paper
  • First Online:
Simulation Tools and Techniques (SIMUtools 2020)

Included in the following conference series:

Abstract

This paper suggests a non-dominated sorting genetic algorithm II (NSGA-II) as a multi-objective framework to construct a multi-objective optimization algorithm and uses the squirrel search algorithm (SSA) as the core evolution strategy. And a multi-objective improved squirrel search algorithm (MOSSA) is proposed. MOSSA establishes an external archive of the population to maintain the elitists in the population. The probability density is applied to limit the size of the merged population to maintain population diversity, based on roulette wheel selection. Also, this paper designs a fitness map** evaluation according to the individual fitness value of each object. Compared with the original SSA, the generational gap is introduced to make the seasonal condition suitable for multi-objective optimization, which could keep the solution from the local con-vergence. This paper simulates MOSSA and other algorithms on multi-objective test functions to analyze the convergence and diversity of PF. It is concluded that MOSSA has a good performance in solving multi-objective problems.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol. Comput. S2210650217305229 (2018)

    Google Scholar 

  2. Wang, Y., Du, T.: A multi-objective improved squirrel search algorithm based on decomposition with external population and adaptive weight vectors adjustment. Physica A: Stat. Mech. Appl. 542, (2020)

    Article  Google Scholar 

  3. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635 (2013)

    Google Scholar 

  4. Gunantara, N.. A review of multi-objective optimization: methods and its applications. Cogent Eng. 5(1), 1502242 (2018)

    Google Scholar 

  5. Guo, Z., Liu, L., Yang, J.: A multi-objective memetic optimization approach for green transportation scheduling. In: 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), IEEE (2015)

    Google Scholar 

  6. Dai, M., Tang, D., Giret, A., Salido, M.A.: Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robot. Comput. Integrated Manuf. 59, 143–157 (2019)

    Article  Google Scholar 

  7. Zaro, F.R., Abido, M.A.: Multi-objective particle swarm optimization for optimal power flow in a deregulated environment of power systems. In: 2011 11th International Conference on Intelligent Systems Design and Applications, IEEE (2019)

    Google Scholar 

  8. Liu, Z., Jiang, D., Zhang, C., et al.: A novel fireworks algorithm for the protein-ligand docking on the AutoDock. Mob. Netw. Appl. 1–12, 53 (2019)

    Google Scholar 

  9. de Villiers, D.I., Couckuyt, I., Dhaene, T.: Multi-objective optimization of reflector antennas using kriging and probability of improvement. In: 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, pp. 985–986. IEEE, July 2017 (2007)

    Google Scholar 

  10. Delgarm, N., Sajadi, B., Kowsary, F., Delgarm, S.: Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO). Appl. Energy 170, 293–303 (2016)

    Article  Google Scholar 

  11. von Lücken, C., Barán, B., Brizuela, C.: A survey on multi-objective evolutionary algorithms for many-objective problems. Comput. Optimization Appl. 1–50 (2014)

    Google Scholar 

  12. Cho, J.H., Wang, Y., Chen, R., et al.: A survey on modeling and optimizing multi-objective systems. IEEE Commun. Surv. Tutorials 19(3), 1867–1901 (2017)

    Article  Google Scholar 

  13. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. Cybern. Trans. IEEE 43(6), 1656–1671 (2013)

    Article  Google Scholar 

  14. Zhang, Q., Li, H.: Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2008)

    Article  Google Scholar 

  15. Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  16. Mashwani, W.K., Salhi, A., Yeniay, O., et al.: Hybrid non-dominated sorting genetic algorithm with adaptive operators selection. Appl. Soft Comput. 56, 1–18 (2017)

    Article  Google Scholar 

  17. Li, K., Wang, R., Zhang, T., et al.: Evolutionary many-objective optimization: a comparative study of the state-of-the-art. IEEE Access 6, 26194–26214 (2018)

    Article  Google Scholar 

  18. Liu, Z., Zhang, C., Zhao, Q., et al.: Comparative study of evolutionary algorithms for protein-ligand docking problem on the AutoDock. International Conference on Simulation Tools and Techniques, pp. 598–607. Springer, Cham (2019)

    Google Scholar 

  19. Azzouz, R., Bechikh, S., Said, L.B.: Dynamic Multi-objective Optimization using Evolutionary Algorithms: A Survey. Recent Advances in Evolutionary Multi-objective Optimization, pp. 31–70. Springer, Cham (2017)

    Book  Google Scholar 

  20. Bechikh, S., Elarbi, M., Said, L.B.: Many-objective Optimization using Evolutionary Algorithms: A Survey. Recent Advances in Evolutionary Multi-objective Optimization, pp. 105–137. Springer, Cham (2017)

    Book  Google Scholar 

  21. Falcón-Cardona, J.G., Coello, C.A.C.: Indicator-based multi-objective evolutionary algorithms: a comprehensive survey. ACM Comput. Surv. (CSUR) 53(2), 1–35 (2020)

    Article  Google Scholar 

  22. Shamshirband, S., Shojafar, M., Hosseinabadi, A.A.R., Abraham, A.: A solution for multi-objective commodity vehicle routing problem by NSGA-II. International Conference on Hybrid Intelligent Systems. IEEE (2015)

    Google Scholar 

  23. Luo, G., Wen, X., Li, H., et al.: An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling. The Int. J. Adv. Manuf. Technol. 91(9–12), 3145–3158 (2017)

    Article  Google Scholar 

  24. Gadhvi, B., Savsani, V., Patel, V.: Multi-objective optimization of vehicle passive suspension system using NSGA-II, SPEA2 and PESA-II. Procedia Technol. 2016(23), 361–368 (2016)

    Article  Google Scholar 

  25. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-report, 103 (2001)

    Google Scholar 

  26. Wang, Y., Han, M.: Research on multi-objective multidisciplinary design optimization based on particle swarm optimization. In: 2017 Second International Conference on Reliability Systems Engineering (ICRSE). IEEE (2017)

    Google Scholar 

  27. Kaoutar, S., Mohamed, E.: Multi-criteria optimization of neural networks using multi-objective genetic algorithm. International Conference on Inteligent Systems & Computer Vision ISCV (2017)

    Google Scholar 

  28. Rosales-Perez, A., Garcia, S., Gonzalez, J.A., Coello, C.A.C., Herrera, F.: An evolutionary multiobjective model and instance selection for support vector machines with pareto-based ensembles. IEEE Trans. Evol. Comput. 21(6), 863–877 (2017)

    Article  Google Scholar 

  29. Juang, Chia-Feng., Jeng, Tian-Lu, Chang, Yu-Cheng: An interpretable fuzzy system learned through online rule generation and multiobjective ACO with a mobile robot control application. IEEE Trans. Cybern. 46(12), 2706–2718 (2017)

    Article  Google Scholar 

  30. Sheikholeslami, F., Navimipour, N.J.: Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance. Swarm and Evol. Comput. 35, 53–64 (2017)

    Article  Google Scholar 

  31. Tian, Y., Cheng, R., Zhang, X., et al.: Diversity assessment of multi-objective evolutionary algorithms: performance metric and benchmark problems [research frontier]. IEEE Comput. Intell. Magazine 14(3), 61–74 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Northeastern University, Shenyang, 110819, People’s Republic of China

    **nyuan Wang, Fanhao Zhang, Zhuoran Liu, Changsheng Zhang, Qidong Zhao & Bin Zhang

Authors
  1. **nyuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fanhao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhuoran Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changsheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qidong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, X., Zhang, F., Liu, Z., Zhang, C., Zhao, Q., Zhang, B. (2021). A Novel Multi-objective Squirrel Search Algorithm: MOSSA. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72795-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72794-9

  • Online ISBN: 978-3-030-72795-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation