An adaptive strategy quantum particle swarm optimization method based on intuitionistic fuzzy entropy and evolutionary game theory

Since premature decline in population diversity is a vital problem in heuristic algorithm optimization, numerous methods enhancing global search ability have been developed to avoid local optimum. However, global exploration strategy diverts resources from exploitation, reducing optimization accuracy. To maintain exploration ability while ensuring accuracy, an adaptive strategy quantum particle swarm optimization method (ASQPSO) based on intuitionistic fuzzy entropy (IFE) and evolutionary game theory (EGT) is proposed in this paper. Firstly, IFE is introduced to quantify algorithm population diversity. Next, this paper proposes several strategies and develops an algorithm structure based on EGT to improve exploration and exploitation performance. Finally, comparison experiments are conducted to verify the performance of ASQPSO. Test results on 23 benchmark functions indicate that the proposed method has better comprehensive performance than the comparison algorithms. This paper researches a feasible way to adjust the diversity of the QPSO method quantitatively and provides a reference for its application in the heuristic algorithms.