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Updated: Apr 20, 2026

Author Spotlight: Investigating the Impact of Aging on Hippocampal-Dependent Spatial Learning

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Published on: February 16, 2024

A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

Liping Sun¹, Yonglong Luo¹, Xintao Ding²

¹College of National Territorial Resources and Tourism, Anhui Normal University, China ; Engineering Technology Research Center of Network and Information Security, Anhui Normal University, China.

Computational Intelligence and Neuroscience

|December 2, 2014

Summary

This study introduces an obstacle distance measure for spatial clustering, enhancing algorithms like the artificial immune clustering with obstacle entity (AICOE). This approach improves clustering in complex environments and public facility location.

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Spatial Separation of Molecular Conformers and Clusters

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Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Area of Science:

Computer Science
Artificial Intelligence
Data Mining

Background:

Traditional spatial clustering relies on Euclidean distance, which is insufficient in environments with obstacles.
Obstacles and facilitators significantly impact spatial relationships and require specialized distance metrics.

Purpose of the Study:

To introduce an innovative obstacle distance measure for spatial clustering under constraints.
To propose the Artificial Immune Clustering with Obstacle Entity (AICOE) algorithm for improved spatial clustering.
To demonstrate the practical applicability of the AICOE algorithm in real-world scenarios.

Main Methods:

Developed a path searching algorithm to approximate obstacle distance, considering both obstacles and facilitators.
Proposed the AICOE algorithm, utilizing obstacle distance as a similarity metric within an artificial immune system framework.
Employed clone selection and elite antibody-based updates for cluster center refinement.

Main Results:

The AICOE algorithm demonstrates superior performance compared to classical clustering algorithms in obstacle-constrained environments.
The obstacle distance measure effectively handles complex spatial relationships influenced by obstacles and facilitators.
Comparative analysis confirmed the AICOE algorithm's ability to achieve global optima and enhanced clustering effects.

Conclusions:

The AICOE algorithm offers a robust solution for spatial clustering in the presence of obstacles.
The obstacle distance measure is a valuable advancement for spatial analysis and data mining.
The model's successful application to public facility location highlights its practical utility and effectiveness.