KQABC Algorithm: A Novel Artificial Bee Colony Algorithm Integrating K-Means Clustering and Q-Learning
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| Format: | Recurso digital |
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2025
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| _version_ | 1866902289053122560 |
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| author | Cao, Yang Yuan, Xiaoquan |
| author_facet | Cao, Yang Yuan, Xiaoquan |
| contents | <p>This repository provides the official C++ implementation of KQABC, an enhanced Artificial Bee Colony (ABC) algorithm that integrates K-Means clustering and Q-learning to address limitations in the standard ABC framework.<br>Key innovations include:<br>(1) K-Means clustering for structured population initialization and dynamic neighborhood construction;<br>(2) Cluster-based local search strategies in the employed bee phase;<br>(3) Three novel search equations (random, elite, and neighborhood strategies) in the onlooker bee phase;<br>(4) Q-learning for adaptive strategy selection based on optimization states.<br>The code package contains:<br>- Complete implementation of KQABC in C++;<br>- 22 benchmark functions from the DFS test suite;<br>- Example configuration and output files for reproduction.<br>Experimental results demonstrate that KQABC significantly outperforms standard ABC and several state-of-the-art variants in solution quality, robustness, and convergence speed across multiple dimensions (D=30, 50, 100).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17284741 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | KQABC Algorithm: A Novel Artificial Bee Colony Algorithm Integrating K-Means Clustering and Q-Learning Cao, Yang Yuan, Xiaoquan Artificial Bee Colony Algorithm Swarm Intelligence Population Clustering K-Means Clustering Neighborhood-Based Search Q-learning Optimization Algorithm Benchmark Functions Metaheuristics <p>This repository provides the official C++ implementation of KQABC, an enhanced Artificial Bee Colony (ABC) algorithm that integrates K-Means clustering and Q-learning to address limitations in the standard ABC framework.<br>Key innovations include:<br>(1) K-Means clustering for structured population initialization and dynamic neighborhood construction;<br>(2) Cluster-based local search strategies in the employed bee phase;<br>(3) Three novel search equations (random, elite, and neighborhood strategies) in the onlooker bee phase;<br>(4) Q-learning for adaptive strategy selection based on optimization states.<br>The code package contains:<br>- Complete implementation of KQABC in C++;<br>- 22 benchmark functions from the DFS test suite;<br>- Example configuration and output files for reproduction.<br>Experimental results demonstrate that KQABC significantly outperforms standard ABC and several state-of-the-art variants in solution quality, robustness, and convergence speed across multiple dimensions (D=30, 50, 100).</p> |
| title | KQABC Algorithm: A Novel Artificial Bee Colony Algorithm Integrating K-Means Clustering and Q-Learning |
| topic | Artificial Bee Colony Algorithm Swarm Intelligence Population Clustering K-Means Clustering Neighborhood-Based Search Q-learning Optimization Algorithm Benchmark Functions Metaheuristics |
| url | https://doi.org/10.5281/zenodo.17284741 |