Abstract
This work presents an efficient multi-objective version of the Pelican Optimization Algorithm (POA) which is recently proposed in the family of meta-heuristic algorithms. It is called a multi-objective Pelican Optimization Algorithm (MOPOA). From the literature, it is observed that the POA performed well on a set of unconstrained classical optimization problems as well as some engineering design problems. To extend its applicability to multi-objective engineering design models, the MOPOA has been proposed and applied for two engineering design models, four bar truss and speed reducer problems. The obtained results are compared with the literature and they proved that the MOPOA is an efficient and robust optimizer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rao, S.S.: Engineering Optimization, 4th edn. John Wiley & Sons Inc, New Jersey (2009)
Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8, 256–279 (2004)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)
Naidu, Y.R., Ojha, A.K.: Solving multiobjective optimization problems using hybrid cooperative invasive weed optimization with multiple populations. IEEE Trans. Syst. Man Cybern. Syst. 48(6), 821–832 (2018)
Ramu Naidu, Y., Ojha, A.K., Susheela Devi, V.: Multi-objective jaya algorithm for solving constrained multi-objective optimization problems. In: Kim, J.H., Geem, Z.W., Jung, D., Yoo, D.G., Yadav, A. (eds.) ICHSA 2019. AISC, vol. 1063, pp. 89–98. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31967-0_11
Abdel-Basset, M., et al.: MOEO-EED: a multi-objective equilibrium optimizer with exploration-exploitation dominance strategy. Knowl. Based Syst. 214, 106717 (2021)
Got, A., Zouache, D., Moussaoui, A.: MOMRFO: multi-objective manta ray foraging optimizer for handling engineering design problems. Knowl. Based Syst. 237, 107880 (2022)
Trojovský, P., Dehghani, M.: Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications. Sensors 22, 855 (2022). https://doi.org/10.3390/s22030855
Mirjalili, S., Jangir, P., Saremi, S.: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl. Intell. 46(1), 79–95 (2016). https://doi.org/10.1007/s10489-016-0825-8
Sadollah, A., Eskandar, H., Kim, J.H.: Water cycle algorithm for solving constrained multi-objective optimization problems. Appl. Soft Comput. 27, 279–298 (2015)
Coello, C.C., Pulido, G.T.: Multiobjective structural optimization using a microgenetic algorithm. Struct. Multidiscip. Optim. 30, 388–403 (2005)
Knowles, J., Corne, D.: The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 98–105. IEEE Press, Piscataway, NJ (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Naidu, Y.R. (2023). Multi-objective Pelican Optimization Algorithm for Engineering Design Problems. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-24848-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24847-4
Online ISBN: 978-3-031-24848-1
eBook Packages: Computer ScienceComputer Science (R0)