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Multigroup cooperative evolutionary optimization algorithm combined with quantum entanglement for cross-field applications

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Publicado en:The Artificial Intelligence Review vol. 58, no. 10 (Oct 2025), p. 327
Autor principal: Lian, Zhaoyang
Otros Autores: Si, Bailu
Publicado:
Springer Nature B.V.
Materias:
Teaching
Swarm intelligence
Quantum entanglement
Evolution
Trajectory optimization
Bionics
Animals
Cooperation
Biological evolution
Genetic algorithms
Food chains
Foraging behavior
Optimization algorithms
Trajectory planning
Evolutionary algorithms
Application
Algorithms
Heuristic
Collective behavior
Robustness
Logistics
Intelligence
Optimization
Tracking
Acceso en línea:Citation/Abstract
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Resumen:Swarm intelligence algorithms are a class of bionic probabilistic heuristic search methods that are inspired by the collective behaviors of biological agents. In this paper, a multigroup cooperative evolutionary optimization algorithm is proposed by referring to the interaction behaviors of species diversity and stability in the ecosystem. First, the group updating mechanism of the traditional seeking and tracking mode with a dynamic population update mechanism is adopted. The multi-population interactive update group and the quantum entanglement update group are introduced to guide the algorithm to gradually approach the global optimal solution. Second, the proposed bionic algorithm is extended for cross-field applications. The algorithm is applied to solve the function optimization problems, as well as problems in four distinct application fields, including robot routing optimization of grid maps, vehicle scheduling optimization of dairy enterprises, location optimization of logistics centers, and plasma trajectory planning optimization. The proposed multigroup cooperative evolutionary optimization algorithm achieves competitive results in these application fields, thus demonstrating its versatility and robustness.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-025-11279-7
Fuente:ABI/INFORM Global