Solution of Multi-dimensional Complex Function Optimization Problems Based on Improved Sparrow Search Algorith
The Sparrow Search Algorithm is an intelligent optimization algorithm characterized by its simple structure and clear principles.The traditional sparrow search algorithm suffers from the problems of insufficient population diversity,the tendency to produce local optima and unstable convergence accuracy in the process of finding the optimal solution.In this paper,an improved sparrow search algorithm is given.Firstly,the population distribution is improved by utilizing an enhanced combination of Cubic and Bernoulli chaotic mappings to enhance population diversity.Secondly,nonlinear adaptive inertia weight and Lévy flight strategy are introduced during the algorithm iteration process to adjust the search range and precision,thus improving the convergence speed and local optimization capability of the algorithm.Furthermore,the predation strategy of the Whale Optimization Algorithm is incorporated to introduce perturbation and prevent getting trapped in local optimum.In conclusion,the improved algorithm was evaluated against traditional Sparrow Search Algorithm and other algorithms on twelve benchmark test functions.Experimental results confirmed that the enhanced algorithm exhibits superior convergence speed and solution accuracy,and it has enhanced local search capabilities.
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维普
