The Differentiated Creative Search (DCS): Leveraging differentiated knowledge-acquisition and creative realism to address complex optimization problems

Abstract

This article introduces differentiated creative search (DCS), a groundbreaking optimization algorithm that revolutionizes traditional decision-making systems in complex environments. In contrast to 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 via the DCS/Xrand/Linnik(α,σ) strategy, which incorporates existing knowledge and Linnik flights. Conversely, the remaining 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. The algorithm's novelty lies in its differentiated knowledge-acquisition, adjusted based on individual team member performance, fostering an environment of continuous learning and adaptation. The paper's contributions are demonstrated through rigorous testing of DCS on various benchmark functions, including CEC2017, classical, and sensor selection problems, as well as real-world applications such as car side impact design, gear train design, and FM sound wave parameter estimation. The results showcase the promising performance of DCS compared to that of existing algorithms, attributable to its innovative approach to problem solving and decision making in complex scenarios. The results and impact section highlights that DCS considerably outperforms traditional optimization algorithms, offering a robust and versatile tool for complex decision-making systems. Its impact is particularly notable in scenarios requiring a balance between innovative solutions and practical decision-making, making DCS a valuable asset in strategic planning and execution across various industries.