Improved Sparrow Search Algorithm based on Multi-strategy Fusion
Aiming at the problems of slow convergence speed,insufficient exploration ability and easy to fall into local optimum of sparrow search algorithm(SSA),an improved sparrow search algorithm(OSSSA)based on multi-strategy fusion was proposed.Firstly,the diversity of population was initialized with the help of Tent chaotic map to improve the quality of initial solution.Secondly,the first stage exploration strategy of osprey algorithm was introduced in the location update of discoverer to improve the exploration ability of population to local search.Finally,cauchy mutation and variable spiral search strategy were introduced to update the follower position to improve the search efficiency and global search performance of the algorithm,reduce the probability of the algorithm falling into the local optimal solution and enhance the global optimization ability of the algorithm.On this basis,eight benchmark functions were simulated to evaluate the optimization performance of the algorithm.Through the analysis of simulated images and data,the improved sparrow search algorithm had greatly improved the convergence speed and optimization accuracy,which verifies the effectiveness of the improved strategy.
万方数据
维普
