Integrating estimation of distribution algorithms versus Q learning into Meta RaPS for solving the 0 1 multidimensional knapsack problem
Abstract Finding near optimal solutions in an acceptable amount of time is a challenge when developing sophisticated approximate approaches. A powerful answer to this challenge might be reached by incorporating intelligence into metaheuristics. We propose integrating two methods into Meta RaPS (Metaheuristic for Randomized Priority Search) which is currently classified as a memoryless metaheuristic. The first method is the Estimation of Distribution Algorithms (EDA) and the second is utilizing a machine learning algorithm known as Q Learning. To evaluate their performance, the proposed algorithms are tested on the 0 1 Multidimensional Knapsack Problem (MKP) Meta RaPS EDA appears to perform better than Meta RaPS Q Learning. However, both showed promising results compared to other approaches presented in the literature for the 0 1 MKP.
Comput. Ind. Eng.