<![CDATA[As the complexity and demand for speed and efficiency in modern computer networks increase, network performance optimization becomes a major challenge in the world of information technology. Issues such as high latency, limited bandwidth, and uneven load distribution often hinder network performance. This research aims to explore and implement heuristic algorithms as a solution to optimize computer network performance. Heuristic algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization, offer adaptive and efficient approaches in seeking optimal solutions to complex problems that cannot be solved exactly in a reasonable time. This research conducts simulations of various network scenarios, focusing on optimal route selection, traffic management, and network resource allocation. The simulation results show that the use of heuristic algorithms can increase throughput, reduce delay, and improve bandwidth utilization efficiency compared to conventional approaches. Additionally, the algorithms used are capable of dynamically adapting to changes in topology and network conditions. These findings demonstrate the great potential of heuristic algorithms in managing future networks that are smarter and more responsive. This research provides theoretical and practical contributions to the development of efficient, flexible, and real-time operationally capable network systems.]]>
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