lpdm_framework - First public release of LPDM Framework

Virtually all metaheuristic algorithms, implicitly or explicitly, aim to achieve a delicate balance between exploration and exploitation in generating populations. Diversity metrics are crucial indicators for both exploration and exploitation, serving as a valuable tool for assessing the performance of metaheuristic algorithms. Although several metrics for diversity have been proposed, most are only suitable for problems with continuous space. In the real world, many optimization problems are defined based on discrete variables, and permutation representation is widely used for modeling real-world problems. Many existing metrics are not applicable to permutation-based problems. Applying other existing metrics to such problems frequently encounters two primary drawbacks: computational cost and effectiveness to represent the actual diversity of the population. In this research, to address these drawbacks, a new exclusive diversity metric is presented for problems that are modeled by permutations. This method calculates diversity by linearizing and partitioning the problem space based on the population distribution in the space and counting the number of individuals in the partitions. Compared to the eleven available metrics (including both permutation-based and usable non-permutation-based metrics) discussed in existing literature, the proposed method demonstrates superior performance, exhibiting acceptable accuracy, stability, sensitivity, and robustness, particularly in the presence of outliers and requiring minimal computational cost. The accuracy of the results of the method is, on average, 35% better than baseline metrics, and the computational cost of the method is nearly 50% better than the fastest available method. These characteristics make the metric a valuable tool for analyzing existing algorithms' behavior or designing new ones.

- C#
Published by faramarzsafi 9 months ago