Intelligent optimization algorithms are widely recognized for the advantages of versatility, speed, and robustness, offering effective solutions to a range of combinatorial optimization problems in smart manufacturing environments. However, the complexity of real-world scheduling problems often leads to excessive execution times, severely limiting the real-time applicability of these algorithms. To address this challenge, this paper presents a hardware framework for configurable intelligent optimization algorithms based on FPGA. The framework leverages the reconfigurable capability of FPGAs, the hybrid characteristics of intelligent optimization algorithms, FPGA’s inherent parallelism, and the integration capabilities of Multi-chip technology. By exploiting algorithmic parallelism and implementing computational processes through logic circuits, the execution time of algorithmic operators is significantly reduced. Additionally, this work explores inter-operator communication to identify more efficient communication mechanisms. Furthermore, this work investigates optimized storage locations and carriers for handling population data, along with more effective methods for data sharing within intelligent optimization algorithms.