Methods › General › Optimization › GBO
Gradient-based optimization
GBO
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
GBO is a novel metaheuristic optimization algorithm. The GBO, inspired by the gradient-based Newton’s method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tendency and accelerate the convergence rate to achieve better positions in the search space. The LEO enables the proposed GBO to escape from local optima. The performance of the new algorithm was evaluated in two phases. 28 mathematical test functions were first used to evaluate various characteristics of the GBO, and then six engineering problems were optimized by the GBO. In the first phase, the GBO was compared with five existing optimization algorithms, indicating that the GBO yielded very promising results due to its enhanced capabilities of exploration, exploitation, convergence, and effective avoidance of local optima. The second phase also demonstrated the superior performance of the GBO in solving complex real-world engineering problems.
- The source codes of GBO are publicly available at https://imanahmadianfar.com/codes/.
Code snippet in the archive: a link on imanahmadianfar.com (archive link, not checked and not linked: not a code host this site links to).
Papers archive 2025-07-28
6 shown of 6, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA 10 May 2023 · 0 repositories · arXiv:2305.06246
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GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems 18 Mar 2023 · 0 repositories · arXiv:2303.12807
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Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO 23 Oct 2022 · 0 repositories · arXiv:2210.12870
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GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting 16 Mar 2022 · 0 repositories · arXiv:2203.08680
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Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems 28 Feb 2022 · 1 repository · arXiv:2202.13510
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Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar 5 Jan 2022 · 0 repositories · arXiv:2201.01479
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Bayesian Optimization | 1 |
| Evolutionary Algorithms | 1 |
| GPU | 1 |
| Generative Adversarial Network | 1 |
| Scene Generation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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