Differentiated Creative Search (DCS)
版本 1.0.1 (3.1 MB) 作者:
Poomin Duankhan
The DCS algorithm leverages teamwork, creativity, and knowledge for superior optimization problem-solving.
This work introduces Differentiated Creative Search (DCS), a groundbreaking optimization algorithm that revolutionizes traditional decision-making systems in complex environments. Differing from conventional differential evolution methods, DCS integrates a unique knowledge-acquisition process with a creative realism paradigm, thereby transforming optimization strategies. The primary aim of DCS is to enhance decision-making efficacy by employing a newly proposed dual-strategy approach that balances divergent and convergent thinking within a team-based framework. High-performing members apply divergent thinking using the DCS/Xrand/Linnik(α,σ) strategy, which incorporates existing knowledge and Linnik flights. Conversely, the rest of the team harnesses convergent thinking through the DCS/Xbest/Current-to-2rand strategy, which combines insights from both the team leader and fellow members. This division of labor, coupled with a strategy tailored to the performance levels of team members, allows for a dynamic and effective decision-making process. The methodology of DCS involves iterative cycles of divergent and convergent thinking, supported by a differentiated knowledge-acquisition process and retrospective assessments.
Related Paper :
The Differentiated Creative Search (DCS): Leveraging differentiated knowledge-acquisition and creative realism to address complex optimization problems. Available at https://doi.org/10.1016/j.eswa.2024.123734
Code Repository:
The MATLAB implementation of DCS is also available at https://github.com/minikku/Differentiated-Creative-Search.
Cite As :
Duankhan, P., Sunat, K., Chiewchanwattana, S., & Nasa-ngium, P. (2024). The Differentiated Creative Search (DCS): Leveraging Differentiated knowledge-acquisition and Creative realism to address complex optimization problems. Expert Systems with Applications, 123734. https://doi.org/10.1016/j.eswa.2024.123734
引用格式
Duankhan, P., Sunat, K., Chiewchanwattana, S., & Nasa-ngium, P. (2024). The Differentiated Creative Search (DCS): Leveraging differentiated knowledge-acquisition and creative realism to address complex optimization problems. Expert Systems with Applications, 252(123734), Article 123734. https://doi.org/10.1016/j.eswa.2024.123734
MATLAB 版本兼容性
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R2023a
兼容 R2021a 到 R2023b 的版本
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Start Hunting!版本 | 已发布 | 发行说明 | |
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1.0.1 | See release notes for this release on GitHub: https://github.com/minikku/Differentiated-Creative-Search/releases/tag/v1.0.1 |
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1.0.0 |
要查看或报告此来自 GitHub 的附加功能中的问题,请访问其 GitHub 仓库。
要查看或报告此来自 GitHub 的附加功能中的问题,请访问其 GitHub 仓库。