A novel binary multi-swarms fruit fly optimisation algorithm for the 0-1 multidimensional knapsack problem

To improve solution quality and accelerate convergence speed of traditional fruit fly optimisation algorithm in solving MKP, a novel binary multi-swarm fruit fly optimisation algorithm (bMFOA) is proposed. It comprises four novelties. Firstly, an item frequency tree (IFT) is constructed based on the idea of frequency pattern mining, and a new search strategy is proposed to obtain heuristic information. Secondly, two new heuristic operators of 'ADD' and 'DROP' are designed according to the obtained heuristic knowledge. Thirdly, a multi-swarm cooperation strategy is presented to strengthen the exploitation capability. To prevent algorithm falling into the local optimum prematurely, a swarm location escape strategy is put forward. To verify the efficiency of bMFOA, it is compared with some existing meta-heuristic methods by solving 58 MKPs from ORLIB. The experimental results show that the bMFOA performs better than existing meta-heuristic methods.