Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems

The moth search algorithm (MS) is a relatively new metaheuristic optimization which mimics the phototaxis and Lévy flights of moths. Being an NP-hard problem, 0–1 multidimensional knapsack problem (MKP) classical multi-constraint complicated combinatorial with numerous applications. In this paper, we present hybrid learning MS (HLMS) by incorporating two mechanisms, global-best harmony (GHS) Baldwinian for solving MKP. (1) GHS guides individuals to more valuable space potential dimensional uses difference between random dimensions generate large jump. (2) change making full use beneficial information other individuals. Hence, mainly provides global exploration works local exploitation. We demonstrate competitiveness effectiveness proposed HLMS conducting extensive experiments on 87 benchmark instances. experimental results show that has better or at least competitive performance against original some state-of-the-art algorithms. addition, parameter sensitivity analyzed important components are investigated understand their impacts algorithm.