Research On Firefly Optimization Algorithm Based On Quantum Computing

Optimization is a classic and important branch of operations research.The swarm intelligent optimization algorithm has become the hot research topic of optimization.The firefly algorithm is an emerging intelligent algorithm,which has the characteristics of simple principle,easy implementation and strong versatility.It has been discussed and studied by many scholars,but the basic firefly algorithm will appear to be slow in convergence and fall into local optimum.Quantum computing theory is an emerging frontier interdisciplinary subject.It has superior high-speed performance such as parallelism and exponential acceleration,and has become a powerful means to improve classical computing.In this paper,quantum states,quantum bits,quantum logic gates,etc.are introduced into the firefly algorithm.The main research contents are as follows:(1)A decreasing inertia weight is designed.The inertia weight is added to the original position term in the position update formula,and the random disturbance term is set.The attractivity formula is equivalently substituted,and a firefly algorithm with improved inertia weight is proposed.(IFA).Write Matlab program,select 4 kinds of test functions,compare and analyze IFA and FA,the convergence effect is obviously improved,and the improved algorithm jumps out of local optimum;(2)The position of the firefly is encoded by the quantum bit,and the position of the firefly is updated by the quantum revolving door.A firefly algorithm based on the quantum revolving door(QFA)is proposed.The Matlab program is written,and six test functions are selected and analyzed in turn.Comparing QFA with FA and AGA,the superiority of QFA algorithm is verified.The improved algorithm has faster convergence speed and the convergence accuracy is obviously improved;(3)Using the QFA algorithm to solve the classic 0-1 knapsack problem in operations research,select an enterprise investment problem and three examples of backpack problems with different items and weights,and verify the efficiency of the quantum firefly algorithm to solve this problem.