Information Utilization Ratio in Heuristic Optimization Algorithms
This work addresses a foundational gap for researchers in optimization by providing a metric to evaluate and improve heuristic algorithms, though it is incremental as it builds on existing concepts.
The paper tackles the lack of a reliable metric for information utilization in heuristic optimization algorithms by defining the Information Utilization Ratio (IUR), which measures the ratio of utilized to acquired information, and provides empirical evidence of its correlation with algorithm performance.
Heuristic algorithms are able to optimize objective functions efficiently because they use intelligently the information about the objective functions. Thus, information utilization is critical to the performance of heuristics. However, the concept of information utilization has remained vague and abstract because there is no reliable metric to reflect the extent to which the information about the objective function is utilized by heuristic algorithms. In this paper, the metric of information utilization ratio (IUR) is defined, which is the ratio of the utilized information quantity over the acquired information quantity in the search process. The IUR proves to be well-defined. Several examples of typical heuristic algorithms are given to demonstrate the procedure of calculating the IUR. Empirical evidences on the correlation between the IUR and the performance of a heuristic are also provided. The IUR can be an index of how finely an algorithm is designed and guide the invention of new heuristics and the improvement of existing ones.