Optimal Configuration Strategy of Heuristic Algorithm in Complex Memory Usage Scenarios

With the advent of the big data era, the demand for processing complex memory usage scenarios is increasing, and traditional memory management strategies are no longer able to meet the requirements of high efficiency. This study focuses on the optimal configuration strategy of heuristic algorithms in complex memory usage environments. Firstly, the article defines the scenarios of complex memory usage and collects the memory access patterns and load characteristics of related systems. Secondly, by comparing and analyzing different heuristic algorithms such as genetic algorithm, simulated annealing, and particle swarm optimization, the article selects the algorithm framework that is most suitable for handling large-scale memory allocation. Through extensive experiments under different memory load scenarios, the data from this study shows that the improved genetic algorithm not only improves memory utilization, but also optimizes system response time and stability under low, medium, and high load conditions. Specifically, under high load conditions, the memory utilization of this algorithm is 3% higher than that of particle swarm optimization algorithm and 5% higher than that of simulated annealing algorithm. These results confirm the effectiveness of the improved genetic algorithm in handling complex dynamic memory allocation requirements, demonstrate its potential for application in highly variable memory management scenarios, and provide new directions and methods for the future development of memory management technology.