A Sustainable Computing-Based Multi-Objective Feeding Scheduling Model for Low-Carbon Factory Aquaculture Using Enhanced NSGA-II in Agricultural Consumer Electronics

Factory aquaculture has emerged as a pivotal approach for farmers and researchers dedicated to sustainable, low-carbon agricultural practices. With the advent of agricultural consumer electronics, such as automated feeders, sensors, and controllers, integrating sustainable computing into these devices is crucial for optimizing resource usage. This paper introduces the Sustainable Computing-Based Multi-Objective Factory Aquaculture Feeding Scheduling problem (MOFAFS). This framework is engineered to automate feeding processes with advanced consumer electronics while simultaneously minimizing energy consumption and reducing feeding time in dynamic aquaculture environments. MOFAFS needed to address three core challenges: sequencing feeding in rearing ponds, distributing automated feeding vehicles, and selecting the most efficient feed supply station. The proposed solution utilizes the Enhanced Non-dominated Sorting Genetic Algorithm (ENSGA-II), which dynamically adapts computational data through variable-length coding and implements a greedy neighborhood search strategy tailored to the problem’s characteristics. Additionally, a weighted fitness approach is employed to refine the selection of Pareto-optimal solutions, determining the best compromise solution. The validity of the model is confirmed through a case study that demonstrates its capability to optimize feeding schedules and reduce the loads on feeding vehicles. Benchmark tests demonstrate that ENSGA-II not only reduces feeding time and operates consistently, but also significantly outperforms PSO, GWO, WOA, and NSGA-II in optimizing maximum operating load.