AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Complex System Modeling and Simulation Article
PDF (1.2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
the School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
the School of Computing, University of Eastern Finland, Kuopio, FI-70211, Finland
Show Author Information

Abstract

With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important. Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems. Thus, this work proposes an improved many-objective pigeon-inspired optimization (ImMAPIO) algorithm with multiple selection strategies to solve many-objective optimization problems. Multiple selection strategies integrating hypervolume, knee point, and vector angles are utilized to increase selection pressure to the true Pareto Front. Thus, the accuracy, convergence, and diversity of solutions are improved. ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III, GrEA, MOEA/D, RVEA, and many-objective Pigeon-inspired optimization algorithm. Experimental results indicate the superiority of ImMAPIO on these test functions.

Keywords

pigeon-inspired optimization algorithmmany-objective optimization problemmultiple selection strategyelite individual retention

References

1
R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in Proc. the 6thInt. Symp. on Micro Machine and Human Science, Nagoya, Japan, 2002, pp. 39–43.
2

Z. H. Cui, J. J. Zhang, D. Wu, X. J. Cai, H. Wang, W. S. Zhang, and J. J. Chen, Hybrid many-objective particle swarm optimization algorithm for green coal production problem, Informat. Sci., vol. 518, pp. 256–271, 2020.
Crossref Google Scholar
3
X. S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J. R. González, D. A. Pelta, C. Cruz, G. Terrazas, N. Krasnogor, eds. Berlin, Germany: Springer, vol. 284, pp. 65–74, 2010.https://doi.org/10.1007/978-3-642-12538-6_6
Crossref
4

H. T. Rauf, M. Hadi, and A. Rehman, Bat algorithm with Weibull walk for solving global optimisation and classification problems, Int. J. Bio-Inspir. Comput., vol. 15, no. 3, pp. 159–170, 2020.
Crossref Google Scholar
5

M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: Optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 26, no. 1, pp. 29–41, 1996.
Crossref Google Scholar
6
X. S. Yang and S. Deb, Cuckoo search via Lévy flights, presented at 2009 World Congress on Nature&Biologically Inspired Computing (NaBIC), Coimbatore, India, 2010, pp. 210–214.https://doi.org/10.1109/NABIC.2009.5393690
Crossref
7

Z. H. Cui, M. Q. Zhang, H. Wang, X. J. Cai, W. S. Zhang, and J. J. Chen, Hybrid many-objective cuckoo search algorithm with Lévy and exponential distributions, Memetic Comput., vol. 12, no. 3, pp. 251–265, 2020.
Crossref Google Scholar
8
X. S. Yang, Firefly algorithm, in Engineering Optimization: An Introduction with Metaheuristic Applications, X. S. Yang, ed. Hoboken, NJ, USA: John Wiley&Sons, Inc., 2010, pp. 221–230.https://doi.org/10.1002/9780470640425.ch17
Crossref
9

J. Zhao, J. J. Tang, A. Y. Shi, T. H. Fan, and L. Z. Xu, Improved density peaks clustering based on firefly algorithm, Int. J. Bio-Inspir. Comput., vol. 15, no. 1, pp. 24–42, 2020.
Crossref Google Scholar
10

D. Karaboga, Artificial bee colony algorithm, Scholarpedia, vol. 5, no. 3, p. 6915, 2010.
Crossref Google Scholar
11

Y. N. Guo, H. Yang, M. R. Chen, J. Cheng, and D. W. Gong, Ensemble prediction-based dynamic robust multi-objective optimization methods, Swarm Evol. Comput., vol. 48, pp. 156–171, 2019.
Crossref Google Scholar
12

Y. N. Guo, J. Cheng, S. Luo, D. W. Gong, and Y. Xue, Robust dynamic multi-objective vehicle routing optimization method, IEEE-ACM Trans. Comput. Biol. Bioinf., vol. 15, no. 6, pp. 1891–1903, 2018.
Crossref Google Scholar
13

H. B. Duan and P. X. Qiao, Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning, Int. J. Intell. Comput. Cybern., vol. 7, no. 1, pp. 24–37, 2014.
Crossref Google Scholar
14

Y. N. Guo, X. Zhang, D. W. Gong, Z. Zhang, and J. J. Yang, Novel interactive preference-based multiobjective evolutionary optimization for bolt supporting networks, IEEE Trans. Evol. Comput., vol. 24, no. 4, pp. 750–764, 2020.
Crossref Google Scholar
15

Y. N. Guo, P. Zhang, J. Cheng, C. Wang, and D. W. Gong, Interval multi-objective quantum-inspired cultural algorithms, Neural Comput. Appl., vol. 30, no. 3, pp. 709–722, 2018.
Crossref Google Scholar
16

Z. H. Cui, Y. R. Zhao, Y. Cao, X. J. Cai, W. S. Zhang, and J. J. Chen, Malicious code detection under 5G HetNets based on a multi-objective RBM model, IEEE Netw., vol. 35, no. 2, pp. 82–87, 2021.
Crossref Google Scholar
17

H. B. Duan, J. X. Zhao, Y. M. Deng, Y. H. Shi, and X. L. Ding, Dynamic discrete pigeon-inspired optimization for multi-UAV cooperative search-attack mission planning, IEEE Trans. Aerosp. Electron. Syst., vol. 57, no. 1, pp. 706–720, 2021.
Crossref Google Scholar
18

H. X. Qiu and H. B. Duan, A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles, Inform. Sci., vol. 509, pp. 515–529, 2020.
Crossref Google Scholar
19

Z. H. Cui, J. J. Zhang, Y. C. Wang, Y. Cao, X. J. Cai, W. S. Zhang, and J. J. Chen, A pigeon-inspired optimization algorithm for many-objective optimization problems, (in Chinese), Sci. China Inf. Sci., vol. 62, no. 7, p. 70212, 2019.
Crossref Google Scholar
20

Z. H. Cui, X. H. Xu, F. Xue, X. J. Cai, Y. Cao, W. S. Zhang, and J. J. Chen, Personalized recommendation system based on collaborative filtering for IoT scenarios, IEEE Trans. Serv. Comput., vol. 13, no. 4, pp. 685–695, 2020.
Crossref Google Scholar
21

Z. H. Cui, F. Xue, S. Q. Zhang, X. J. Cai, Y. Cao, W. S. Zhang, and J. J. Chen, A hybrid Block Chain-based identity authentication scheme for Multi-WSN, IEEE Trans. Serv. Comput., vol. 13, no. 2, pp. 241–251, 2020.
Crossref Google Scholar
22

X. J. Cai, S. J. Geng, D. Wu, J. H. Cai, and J. J. Chen, A multicloud-model-based many-objective intelligent algorithm for efficient task scheduling in internet of things, IEEE Int. Things J., vol. 8, no. 12, pp. 9645–9653, 2021.
Crossref Google Scholar
23

C. Li and H. B. Duan, Target detection approach for UAVs via improved pigeon-inspired optimization and edge potential function, Aerosp. Sci. Technol., vol. 39, pp. 352–360, 2014.
Crossref Google Scholar
24

H. B. Duan, H. X. Qiu, and Y. M. Fan, Unmanned aerial vehicle close formation cooperative control based on predatory escaping pigeon-inspired optimization, (in Chinese), Sci. Sin. Tech., vol. 45, no. 6, pp. 559–572, 2015.
Crossref Google Scholar
25

B. Zhang and H. B. Duan, Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment, IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 14, no. 1, pp. 97–107, 2017.
Crossref Google Scholar
26
H. Arshad, S. Batool, Z. Amjad, M. Ali, S. Aimal, and N. Javaid, Pigeon inspired optimization and enhanced differential evolution using time of use tariff in smart grid, presented at the 9th Int. Conf. Intelligent Networking and Collaborative Systems (INCoS 2017), Tronto, Canada, 2017, pp. 563–575.https://doi.org/10.1007/978-3-319-65636-6_51
Crossref
27
X. X. Sun, J. S. Pan, S. C. Chu, P. Hu, and A. Q. Tian, A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks, Int. J. Distrib. Sens. Netw. vol. 16, no. 6, p. 155014772093274, 2020.https://doi.org/10.1177/1550147720932749
Crossref
28

H. X. Qiu and H. B. Duan, Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design, (in Chinese), Sci. China Tech. Sci., vol. 58, no. 11, pp. 1915–1923, 2015.
Crossref Google Scholar
29

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.
Crossref Google Scholar
30

H. B. Duan, M. Z. Huo, and Y. H. Shi, Limit-cycle-based mutant multiobjective pigeon-inspired optimization, IEEE Trans. Evol. Comput., vol. 24, no. 5, pp. 948–959, 2020.
Crossref Google Scholar
31

Y. Hu, J. Wang, J. Liang, K. J. Yu, H. Song, Q. Q. Guo, C. T. Yue, and Y. L. Wang, A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm, (in Chinese), Sci. China Inf. Sci., vol. 62, no. 7, p. 70206, 2019.
Crossref Google Scholar
32

Q. Z. Lin, S. B. Liu, Q. L. Zhu, C. Y. Tang, R. Z. Song, J. Y. Chen, C. A. C. Coello, K. C. Wong, and J. Zhang, Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems, IEEE Trans. Evol. Comput., vol. 22, no. 1, pp. 32–46, 2018.
Crossref Google Scholar
33

M. A. Dulebenets, Archived elitism in evolutionary computation: Towards improving solution quality and population diversity, Int. J. Bio-Inspir. Comput., vol. 15, no. 3, pp. 135–146, 2020.
Crossref Google Scholar
34

J. Bader and E. Zitzler, HypE: An algorithm for fast hypervolume-based many-objective optimization, Evol. Comput., vol. 19, no. 1, pp. 45–76, 2011.
Crossref Google Scholar
35

X. Y. Zhang, Y. Tian, and Y. C. Jin, A knee point-driven evolutionary algorithm for many-objective optimization, IEEE Trans. Evol. Comput., vol. 19, no. 6, pp. 761–776, 2015.
Crossref Google Scholar
36

Y. Xiang, Y. R. Zhou, M. Q. Li, and Z. F. Chen, A vector angle-based evolutionary algorithm for unconstrained many-objective optimization, IEEE Trans. Evol. Comput., vol. 21, no. 1, pp. 131–152, 2017.
Crossref Google Scholar
37
K. Li, J. H. Zheng, M. Q. Li, C. Zhou, and H. Lv, A novel algorithm for non-dominated hypervolume-based multiobjective optimization, presented at 2009 IEEE Int. Conf. Systems, Man and Cybernetics (SMC’2009), San Antonio, TX, USA, 2009, pp. 5220–5226.https://doi.org/10.1109/ICSMC.2009.5345983
Crossref
38

Q. Z. Lin, J. Q. Li, Z. H. Du, J. Y. Chen and Z. Ming, A novel multi-objective particle swarm optimization with multiple search strategies, Eur. J. Oper. Res., vol. 247, no. 3, pp. 732–744, 2015.
Crossref Google Scholar
39
K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, Scalable multi-objective optimization test problems, in Proc. 2002 Congress on Evolutionary Computation, Honolulu, HI, USA, 2002, pp. 825–830.
40
S. Huband, P. Hingston, L. Barone, and L. While, A review of multiobjective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., vol. 10, no. 5, pp. 477–506, 2006.https://doi.org/10.1109/TEVC.2005.861417
Crossref
41
K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints, IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 577–601, 2014.https://doi.org/10.1109/TEVC.2013.2281535
Crossref
42
S. X. Yang, M. Q. Li, X. H. Liu, and J. H. Zheng, A grid-based evolutionary algorithm for many-objective optimization, IEEE Trans. Evol. Comput., vol. 17, no. 5, pp. 721–736, 2013.https://doi.org/10.1109/TEVC.2012.2227145
Crossref
43
Q. F. Zhang and H. Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712–731, 2007.https://doi.org/10.1109/TEVC.2007.892759
Crossref
44
R. Cheng, Y. C. Jin, M. Olhofer, and B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization, IEEE Trans. Evol. Comput., vol. 20, no. 5, pp. 773–791, 2016.https://doi.org/10.1109/TEVC.2016.2519378
Crossref
45

E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. Da Fonseca, Performance assessment of multiobjective optimizers: An analysis and review, IEEE Trans. Evol. Comput., vol. 7, no. 2, pp. 117–132, 2003.
Crossref Google Scholar
46

E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE Trans. Evol. Comput., vol. 3, no. 4, pp. 257–271, 1999.
Crossref Google Scholar
Complex System Modeling and Simulation
Volume 1 Issue 4,
December 2021
Pages 291-307
DOI: 10.23919/CSMS.2021.0023

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Cite this Report
Close
Close
Cite this article:
Cui Z, Zhao L, Zeng Y, et al. Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems. Complex System Modeling and Simulation, 2021, 1(4): 291-307. https://doi.org/10.23919/CSMS.2021.0023

1127

Views

66

Downloads

17

Crossref

0

Web of Science

18

Scopus

Altmetrics
Received: 06 August 2021
Revised: 04 September 2021
Accepted: 13 September 2021
Published: 31 December 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).