The 0-1 multidimensional knapsack problem(MKP) is a classical NP-hard
combinatorial optimization problem. In this paper, we propose a novel heuristic
algorithm simulating evolutionary computation and large neighbourhood search
for the MKP. It maintains a set of solutions and abstracts information from the
solution set to generate good partial assignments. To find high-quality
solutions, integer programming is employed to explore the promising search
space specified by the good partial assignments. Extensive experimentation with
commonly used benchmark sets shows that our approach outperforms the state of
the art heuristic algorithms, TPTEA and DQPSO, in solution quality. It finds
new lower bound for 8 large and hard instance