An adaptive mutation-dissipation binary particle swarm optimisation for multidimensional knapsack problem

Particle swarm optimisation (PSO) algorithm is a novel swarm intelligence optimisation algorithm and has been widely researched and applied in many fields. However, the traditional PSO and most of its variants are designed for optimisation problems in continuous space which cannot be used to solve discrete binary optimisation problems. To make up for it, the discrete binary particle swarm algorithm has been proposed for binary-code optimisation problems, but its optimisation ability is not ideal. In this paper, we propose a novel adaptive mutation-dissipation binary particle swarm optimisation (MDBPSO) for tackling multidimensional knapsack problem (MKP). In MDBPSO, the adaptive mutation operator and dissipation operator are introduced to enhance the local search ability and keep the diversity of swarm. The experimental results on benchmark MKP demonstrate that the proposed MDBPSO has better optimisation ability and is easy to implement.