The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning
Abstract
Purpose: – The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems. Design/methodology/approach: – The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted. Findings: – The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases. Originality/value: – In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.
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Citations
Cites
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A new optimizer using particle swarm theory
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AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION
Karaboğa, D. (2005) · Cited by 5,466
Distributed Optimization by Ant Colonies
Colorni, A., Dorigo, M., Maniezzo, V., Varela, F. J., & Bourgine, P. (1992) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · Cited by 2,748
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Inspiration for optimization from social insect behaviour
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Magnetoreception and its trigeminal mediation in the homing pigeon
Mora, C. V., Davison, M., Martin Wild, J., & Walker, M. M. (2004) · Nature · doi:10.1038/nature03077 · Cited by 279
?Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration
Duan, H., Luo, Q., Shi, Y., & Ma, G. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2264577 · Cited by 243
Self-citationAn Optimization Algorithm Based on Brainstorming Process
Shi, Y. (2011) · International Journal of Swarm Intelligence Research · doi:10.4018/ijsir.2011100103 · Cited by 224
Cited by
Decision Making in Multiagent Systems: A Survey
Rizk, Y., Awad, M., & Tunstel, E. W. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2840971 · Cited by 219
Chen, L., Song, Y., Lopes, A. M., Bao, X., Zhang, Z., & Lin, Y. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3291501 · Cited by 85
Applications of Metaheuristics in Reservoir Computing Techniques: A Review
Bala, A., Ismail, I., Ibrahim, R., & Sait, S. M. (2018) · IEEE Access · doi:10.1109/access.2018.2873770 · Cited by 53
Metadata
- Authors
- Duan, H., & Qiao, P.
- Venue
- International Journal of Intelligent Computing and Cybernetics
- Publisher
- Emerald
- Published
- 2014-03-04
- Volume
- 7
- Issue
- 1
- Pages
- 24-37
- ISSN
- 1756-378X (ISSN-L)
- OpenAlex
- W2122032069
- Cited by
- 601
- References
- 16
- FWCI
- 27.07
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. https://app.cochat.ai/papers/10.1108/ijicc-02-2014-0005
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- Duan, H., & Qiao, P. (2014). Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. International Journal of Intelligent Computing and Cybernetics. https://doi.org/10.1108/ijicc-02-2014-0005
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
