Opposition-based learning monarch butterfly optimization with Gaussian perturbation for large-scale 0-1 knapsack problem

Monarch butterfly optimization (MBO) has become an effective optimization technique for function optimization and combinatorial optimization. In this paper, a generalized opposition-based learning (OBL) monarch butterfly optimization with Gaussian perturbation (OMBO) is presented, in which OBL strategy is used on half individuals of the population in the late stage of evolution and Gaussian perturbation acts on part of the individuals with poor fitness in each evolution. OBL guarantees the higher convergence speed of OMBO and Gaussian perturbation avoids to be stuck at a local optimum. In order to test and verify the effectiveness of the proposed method, three groups of 15 large-scale 0-1 KP instances from 800 to 2000 dimensions are used in our studies. The experimental results indicate that OMBO can find high-quality solutions.