Unlocking operational excellence: A deep dive into a communication-driven multi-strategy state transition algorithm for industrial process optimization

The optimization of industrial processes is crucial for enhancing operational safety, productivity, and energy efficiency. However, the increasing complexity of industrial processes poses challenges to the applicability of intelligent optimization algorithms, such as the state transition algorithm. The main challenges include limited individual diversity, the intricate task of escaping local optima, and the underutilization of solutions beyond predefined boundaries. To overcome these challenges, this study proposes a novel intelligent stochastic optimization algorithm called the communication-driven multi-strategy state transition algorithm (CDMS-STA). CDMS-STA incorporates three self-designed strategies: communication, self-learning, and cross-boundary solution processing. Specifically, the communication strategy enhances the diversity of candidate solutions by promoting information exchange and sharing among individuals. The self-learning strategy facilitates adaptive adjustments based on historical information. The cross-boundary solution processing strategy maximizes the utilization of solutions beyond defined boundaries. To evaluate the effectiveness of the proposed algorithm, extensive experiments were conducted on a numerical sample case and two chemical process cases. The experimental results demonstrate that CDMS-STA outperforms several state-of-the-art optimization methods to find the optimal solution, which also validates the potential of the proposed algorithm for practical application in industrial process optimization. •A novel intelligent communication-driven multi-strategy state transition algorithm (CDMS-STA) is proposed for the applicability of industrial process optimization.•The proposed algorithm emphasizes a self-designed communication-driven strategy as its core concept to facilitate effective information exchange and sharing by establishing a communication mechanism.•This study designs a self-learning strategy for autonomous external knowledge acquisition through continuous learning and environmental adaptation.•Extensive experiments conducted in a numerical example case and two chemical process cases demonstrate that the proposed algorithm outperforms several state-of-the-art optimization methods.