Comparison of Metaheuristics in Solving the Knapsack Problem: An Experimental Analysis

Abstract

Objective: Through statistical analysis using ANOVA, compare the obtained results and processing time of the metaheuristics Local Search, Tabu Search, and Genetic Algorithm programmed in Python language for application in the Knapsack Problem among the described instances. Method: The method used was modeling in order to compare randomly generated instances where the metaheuristics were programmed in Python language, inserted in Google Colaboratory, and executed in the cloud. Results and Conclusion: Analysis of Variance (ANOVA) was employed as there were three samples with paired instances to ensure conclusion validation. It was observed that, for the instances and interruption parameters of the metaheuristics used, the Genetic Algorithm generated more satisfactory results than the other metaheuristics. Research Implications: Provides relevant information about the effectiveness and performance of metaheuristic techniques, contributing to the evolution of the field of Operations Research by guiding the choice of approaches in practical applications and promoting collaboration and scientific replicability.