Iterated Tabu Search Algorithm for the Multidemand Multidimensional Knapsack Problem

The multidemand multidimensional knapsack problem (MDMKP) is a classic NP-hard combinatorial optimization problem with a number of real-world applications. In this paper, we propose an iterated tabu search (ITS) algorithm for solving this computationally intractable problem, by integrating two solution-based tabu search procedures aiming to locally improve the solutions and a perturbation operator aiming to jump out of local optimum traps. The performance of proposed algorithm was assessed on 54 benchmark instances commonly used in the literature, and the experimental results show that the proposed algorithm is very competitive compared to the state-of-the-art algorithms in the literature. In particular, the proposed ITS algorithm improved the best known results in the literature for 27 out of 54 instances.