optimization problems exist widely in scientific research and engineering practice,and as the industrial development moves toward a new type,comprehensive,and continuous direction,the optimization problems faced in the industrial production process have multi-objective solutions,Strong nonlinear,high-variable dimensions.The heuristic intelligent algorithm opens up a new solution to solve the complex optimization problem.Therefore,the research on intelligent algorithm has important theoretical significance and application value.The frog leaping algorithm(SFLA)is a new heuristic intelligent algorithm inspired by the predatory behavior of frogs.The algorithm simulates frog predation to make particle search in the feasible domain space.It has the characteristics of simple structure,few pa-rameters,robustness.At present,it is widely concerned by scholars at home and abroad and has become a hot topic in the field of intelligent algorithm optimization research.How-ever,this algorithm also has its own defects,such as the low accuracy of the solution,the slow convergence speed,and the premature defects.Therefore,in order to enhance the performance of the algorithm and make up for the lack of algorithm,further study and optimization of the algorithm are needed.In order to overcome the shortcomings of Shuffled Frog Leaping Algorithm(SFLA)in solving multimodal function optimization problems which includes easily falling into local minimum,low accuracy and difficultly searching extreme points as many as possi-ble,a novel bidirectional immune shuffled frog leaping algorithm based on circle derived mutation(BISFLA)is proposed.This algorithm in each loop iteration employs the form of "local-global" based on bidirectional evolution mechanism to searching in the feasible zone,and then uses double Control Mutative Clonal Selection Algorithm(DCSA)to im-prove the accuracy of the sub-optimal solution.The function is rotated to further verify the performance of the algorithm.Simulation results show that compared with the original frog jumping algorithm,the optimization accuracy of the algorithm and the number of extremum points are significantly improved while the convergence speed is guaranteed.X-ray pulsar navigation(XNAV)is a widely used astronomical autonomous navigation method.It obtains spacecraft parameters in deep air through time of arrival(TOA).The best way to calculate TOA is through time delay estimation.In this paper,based on the tradi-tional bispectrum algorithm,an algorithm based on the improved frog leaping algorithm is proposed to estimate the time delay of the X-ray pulsar integrated profile.The proposed method is used to extract the bispectral feature points of the standard pulsar integrated pulse profile by the Shuffled Frog Leaping Algorithm on the ground control center,and store these feature points into the spacecraft's database.Then to estimate the time delay faster,it is only necessary to calculate the self-bispectrum and the cross-bispectrum of the standard pulsar integrated pulse profile(SIPP)and the observed pulsar integrated pulse profile(OIPP)at the extracted bispectral feature points during flying.Finally,simulation experiments show that the proposed algorithm preserves the high accuracy of the traditional bispectrum algorithm in time delay estimation and greatly enhances the real-time performance of the navigation algorithm. |