October 2, 2025International Journal of Swarm Intelligence ResearchOpen Access

Adaptive Particle Swarm Optimizer With Kernel Density-Based Decoupled Exploration and Exploitation for Large-Scale Optimization

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Authors

ZLZ.-H. Liu ZGZhiyu Gan SZS. K. Zhou

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Implication

This algorithm demonstrates improved balance in exploration and exploitation strategies, suggesting enhanced optimization performance in complex scenarios.

Key Points

The APSODEE-KD algorithm effectively addresses challenges in large-scale optimization, improving performance.
Utilizing kernel density estimation, it enhances swarm distribution analysis based on local crowding effects.
A tournament selection strategy facilitates decoupled exploration and exploitation for better particle learning.
Experimental results from benchmark suites indicate that APSODEE-KD surpasses current state-of-the-art optimization methods.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68de5da783cbc991d0a20a72 https://doi.org/10.4018/ijsir.389667

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Implication

This algorithm demonstrates improved balance in exploration and exploitation strategies, suggesting enhanced optimization performance in complex scenarios.

Adaptive Particle Swarm Optimizer With Kernel Density-Based Decoupled Exploration and Exploitation for Large-Scale Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

Adaptive Particle Swarm Optimizer With Kernel Density-Based Decoupled Exploration and Exploitation for Large-Scale Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

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