Exploring Initialization Strategies for Metaheuristic Optimization: Case Study of the Set-Union Knapsack Problem

In recent years, metaheuristic methods have shown remarkable efficacy in resolving complex combinatorial challenges across a broad spectrum of fields. Nevertheless, the escalating complexity of these problems necessitates the continuous development of innovative techniques to enhance the performance and reliability of these methods. This paper aims to contribute to this endeavor by examining the impact of solution initialization methods on the performance of a hybrid algorithm applied to the set union knapsack problem (SUKP). Three distinct solution initialization methods, random, greedy, and weighted, have been proposed and evaluated. These have been integrated within a sine cosine algorithm employing k-means as a binarization procedure. Through testing on medium- and large-sized SUKP instances, the study reveals that the solution initialization strategy influences the algorithm's performance, with the weighted method consistently outperforming the other two. Additionally, the obtained results were benchmarked against various metaheuristics that have previously solved SUKP, showing favorable performance in this comparison.

Automated summary

This paper examines the impact of solution initialization methods on the performance of a hybrid algorithm applied to the set union knapsack problem (SUKP). The weighted method outperforms random and greedy methods, demonstrating its effectiveness in improving algorithm performance. The results are compared with other metaheuristics that have previously solved SUKP, further showcasing the favorable performance of the weighted method.