Research And Application Of New Fruit Fly Optimization Algorithm

With the continuous development of modern society,computer technology has been developed from simple applications that can meet people's needs from the beginning to complex,multifunctional,and highly efficient intelligent artificial technologies.During this period,computer technology has achieved leapfrog development,and Continuous optimization is still being performed,just to pursue higher performance and bring better convenience and experience to people.During this period,intelligent meta-heuristics were born.Fruit Fly Optimization Algorithm(FOA)is a meta-heuristic intelligent optimization algorithm.FOA is a creative work produced by researcher Pan Wenchao by observing the foraging behavior of fruit flies.Although the structure is simple but not deaf,the parameters are few and easy to master.The time complexity is suitable for major applications.Since the algorithm was proposed,it has been widely used and studied by many scholars.However,the algorithm itself has some shortcomings.For example,it is easy to fall into the local optimal value during the iteration process,and it is prone to prematureness when solving the multi-extremity problem in a high-dimensional environment.The accuracy value of the solution data is not high and cannot reach Expected effect,etc.In order to solve the above problem,this paper proposes a new fruit fly optimization algorithm(Self-Change Step Size Fruit Fly Optimization Algorithm,SCFOA),and applies the algorithm to solving the 0-1knapsack problem and image fusion.The main research contents are as follows:(1)This paper proposes an improved step size control strategy to strengthen the optimization ability of FOA.The step distance is mainly adjusted in real time using the rate of change of the concentration difference.When the algorithm is in the early stage of iterative optimization,the change rate of the concentration difference is large at this time,indicating that the fruit fly population is far away from the target,and the step size should be increased to improve the global optimization rate.At the later stage of the iteration,the change rate of the concentration difference becomes smaller,indicating that the fruit fly population is closer to the target value.At this time,the step size value should be reduced to improve the local optimization accuracy.In order to prevent early iteration from falling into the local optimum prematurely,a jump mechanism is added to the algorithm proposed in this paper.By using thet-distribution characteristics and randomness,the algorithm jumps out of the local optimum and finally reaches a better data set.(2)In order to verify the optimal performance of SCFOA,SCFOA is used to solve the0-1 knapsack problem.The 0-1 knapsack problem is a non-deterministic polynomial problem.There are many solutions and the dimensions are too large.Traditional solutions cannot be used.To solve this problem,this article uses SCFOA to solve the 0-1 knapsack problem,and uses the optimization ability of SCFOA to find the optimal solution of the 0-1 knapsack problem.In order to prove that SCFOA can effectively solve this problem,this paper uses eight classic 0-1 The knapsack dataset is compared with other four intelligent optimization algorithms for the same environment test,which is used to prove the validity of SCFOA in dealing with the 0-1 knapsack problem.(3)Applying SCFOA to wavelet transform image fusion,the wavelet transform is good at cutting the source image set into high and low frequency levels,and different processing schemes are used for different part levels,and appropriate amount of parameters will be used in the solution to control the result set Fusion quality.In the past,the selection of parameters was set according to human experience,but the parameter values set in this way will vary with the change of the image and cannot be dynamically adjusted and adapted in real time.To this end,SCFOA is used to optimize the parameter values,and the image information entropy value is used as the effect evaluation of image fusion.It is confirmed through most experiments that SCFOA can enhance the effect of wavelet transform image fusion.