Multi-objective scheduling problems are inherently complex due to the need to balance competing objectives, such as minimizing the total weighted completion time, reducing the number of delayed jobs, and minimizing the maximum weighted delay. To address these challenges, this article introduces the meerkat clan algorithm (MCA), inspired by the dynamic, cooperative, and adaptive behaviors of meerkats, which enhances the exploration and exploitation of solution spaces. The MCA is further integrated with the traditional branch-and-bound (BAB) method, utilizing it as an upper bound to significantly improve the accuracy and efficiency of the solutions. Comprehensive computational experiments were conducted to evaluate the MCA’s performance against state-of-the-art algorithms, including the bald eagle search optimization algorithm (BESOA) and the standalone BAB method. The MCA demonstrated superior scalability and efficiency, effectively solving problems involving up to n = 30,000 jobs, whereas the BESOA was limited to handling instances with n = 1,000 jobs. Additionally, the integration of MCA with the BAB method achieved exceptional precision and efficiency for smaller problem instances, handling up to n = 13 jobs effectively. The results underscore the MCA algorithm’s potential as a robust solution for multi-objective scheduling problems, combining speed and accuracy to outperform traditional methods. Moreover, the hybrid approach of integrating MCA with BAB provides a flexible and versatile framework capable of addressing a wide range of scheduling scenarios, from small-scale to large-scale applications. These findings position the MCA as a transformative tool for solving complex scheduling problems in both theoretical and practical domains.

The flow shop scheduling problem in Multiprocessor-System-on-Chip (MPSoC) architectures presents challenges for traditional optimization algorithms, especially when addressing multiple conflicting objectives. Hence, advanced optimization approaches are required to tackle these objectives simultaneously. Therefore, this research aims to propose and evaluate a new optimization approach based on the integration of the Fire Hawk Optimizer with the Smart Battery Scheduling Algorithm (FHO-SBSA) to address the multi-objective (make span, CPU time, global average delay, network throughput, and total energy consumption) flow shop scheduling problem in MPSoC systems. To evaluate the performance of the FHO-SBSA optimization approach, two benchmark applications were selected, with ten instances assessed. The FHO-SBSA was compared against Practical Swarm Optimization (PSO) and Genetic Algorithm (GA) in solving the MOFSS problem. Additionally, the optimization effectiveness of FHO-SBSA was evaluated at two distinct levels: computational efficiency at the MPSoC IP core level and communication efficiency with the Network-on-Chip (NoC) architecture. The comparative analysis included performance metrics both before and after fine-tuning the algorithm, with a specific focus on area occupancy. The evaluation was conducted across multiple mesh grid sizes to assess the impact of the optimization on system performance comprehensively. Results: Experimental results show that FHO-SBSA outperforms GA and PSO in all measured aspects. It achieved makespan values ranging from 971.0 to 1332.0 and maintained low CPU time between 0.0315s and 0.0807s. In comparison, GA and PSO yielded higher makespan values and required longer processing times. Additionally, FHO-SBSA demonstrated improved energy consumption and communication efficiency. Implications: By applying FHO-SBSA, MOFSS can be optimally solved in NoC-based MPSoC systems, where performance is parallel to power consumption. Significant for high-performance energy-critical real-time applications in next-generation MPSoC design, enhancing system performance in computationally challenging tasks.