Differentiated Creative Search (DCS)

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

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, 252(123734), Article 123734. https://doi.org/10.1016/j.eswa.2024.123734