Biomimetic swarm fission driven algorithm with preassigned target subgroup size

Inspired by killer whale hunting strategies, this study presents a biomimetic algorithm for controlled subgroup fission in swarms. The swarm agents adopt the classic social force model with some practical modifications. The proposed algorithm consists of three phases: cluster selection phase via a constrained K-means algorithm, driven phase with strategic agent movement, including center pushing, coordinated oscillation, and flank pushing by specialized driven agents, and judgment phase confirming subgroup separation using the Kruskal algorithm. Simulation results confirm the algorithm's high success rate and efficiency in subgroup division, demonstrating its potential for advancing swarm-based technologies.

Authors

Institutions

Publication Details

Journal
Bioinspiration & Biomimetics
Published
2025-01-29
DOI
https://doi.org/10.1088/1748-3190/adaff5
Citations
2
Primary Topic
Metaheuristic Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
4.96

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Biomimetic swarm fission driven algorithm with preassigned target subgroup size

He Cai, Huanli Gao, D. Zhou, Zixin Bei et al.
2 citations
Bioinspiration & Biomimetics
Metaheuristic Optimization Algorithms Research
4.96
article

Biomimetic swarm fission driven algorithm with preassigned target subgroup size

He Cai, Huanli Gao, D. Zhou, Zixin Bei, Hao Wang
article en
2 citations

Abstract

Inspired by killer whale hunting strategies, this study presents a biomimetic algorithm for controlled subgroup fission in swarms. The swarm agents adopt the classic social force model with some practical modifications. The proposed algorithm consists of three phases: cluster selection phase via a constrained K-means algorithm, driven phase with strategic agent movement, including center pushing, coordinated oscillation, and flank pushing by specialized driven agents, and judgment phase confirming subgroup separation using the Kruskal algorithm. Simulation results confirm the algorithm's high success rate and efficiency in subgroup division, demonstrating its potential for advancing swarm-based technologies.

Bioinspiration & BiomimeticsVol. 20(2)
Ministry of Education of the People's Republic of China (CN), Key Laboratory of Guangdong Province (CN), South China University of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 6%
Metaheuristic Optimization Algorithms Research
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.