Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains challenging task. In this study, we propose Modified Artificial Bee Colony algorithm (MABC) solve MMKP. MABC employs surrogate relaxation, Hamming distance, and tabu list enhance local search process exploit neighborhood information. We evaluated performance of on standard benchmark instances compared it with several state-of-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS 2 D-QPSO. experimental results reveal produces highly competitive in terms best found, achieving approximately 2% optimal trivial (milliseconds) CPU time. Kruskal-Wallis test revealed there was no statistically significant difference objective function values between other algorithms (H = 0.31506, p 0.98882). efficiency, showed 84.90850, 0), indicating exhibited significantly better efficiency (with shorter execution times) than did. Along these findings, ease implementation small number control parameters make our approach adaptive large-scale systems.