Energy-Load Collaborative Optimization Method Based on Fuzzy-SM-GA in Consumer Electronics

With the continuous development of the consumer electronics field, the energy and load demands of consumer electronics are increasing day by day, and innovative methods are urgently needed to achieve effective synergy between energy consumption and device load. This article proposes an energy load collaborative optimization method based on fuzzy slime mold genetics. In order to address the non flexibility of static energy and load management strategies, this method adopts a second-order parallel processing approach, with unified scheduling for non delay sensitive tasks and priority processing scheduling for delay sensitive tasks. Specifically, through fuzzy reasoning, the energy consumption and load characteristics of different consumer electronics service tasks under various operating conditions are modeled and used as the objective of collaborative optimization problems. Design an adaptive fuzzy slime mold genetic algorithm that adopts a cross propagation approach to eliminate the impact of service order in consumer electronics tasks on overall optimization performance, reduce the generation of irregular solutions in iterations, seek optimal load allocation solutions, reduce energy consumption, and improve resource utilization. The experimental results show that our proposed method can reduce energy consumption, improve resource utilization, and have good convergence speed for the service demand of multi consumer electronics tasks.