Research On Improvement Of Sparrow Search Algorithm

Realistic optimization problems have the characteristics of numerous variables,nonlinear constraints,and numerous local optimal solutions.Traditional optimization methods are often difficult to apply.Group intelligence algorithms are widely used due to their simplicity,flexibility,and low computational costs.Sparrow search algorithm is a swarm intelligent optimization algorithm proposed to simulate the foraging behavior of sparrow populations.It has the characteristics of simple structure,easy implementation,and strong local search ability.Since its inception,it has been studied by many scholars and has been applied to practical optimization problems in many fields.Aiming at the shortcomings and shortcomings of the sparrow search algorithm,this paper improves the algorithm by integrating corresponding improvement strategies,and proposes two improved sparrow search algorithms.Experiments have verified the performance and practicality of the proposed improved algorithm.The main work of this article is as follows:Aiming at the problems of lack of diversity and falling into local optimum during the iteration process of sparrow search algorithm,a modified sparrow search algorithm(ISSA)based on multi strategy fusion was proposed by integrating the mutation operator and multi strategy individual perturbation mechanism of differential evolution algorithm into the sparrow search algorithm.On the basis of the original sparrow search algorithm,the mutation operator of the weighted mutation differential evolution algorithm is combined to improve the search efficiency of the algorithm and enhance the diversity of the population.A multistrategy individual perturbation mechanism is added to enhance the global search ability and the ability to jump out of local optima of the algorithm.In order to verify the algorithm performance of ISSA,a comparative experiment was conducted with six population intelligent algorithms on 12 benchmark test functions.The results show that ISSA has higher optimization accuracy and faster convergence speed compared to the other six algorithms,and has consistent optimization performance on high-dimensional function functions.Statistical testing of the algorithm optimization results shows that there is a significant difference in the algorithm performance between ISSA and the six algorithms,ISSA has better algorithm performance.The effectiveness and practical effects of the two improved strategies are verified through comparative experiments on the optimization performance of the improved sparrow search algorithm incorporating a single improved strategy.Finally,ISSA is applied to practical optimization problems of UAV path planning and tension spring design,and experimental results verify the performance and practicality of ISSA algorithm.In order to improve the global search ability of sparrow search algorithms and utilize the information of discoverers’ groups,a hybrid sparrow search algorithm(HSSA)combining the dynamic step coefficient Levy flight and elite individual firefly algorithm search was proposed.Based on the initial sparrow search algorithm,the dynamic step coefficient Levy flight is used to improve the finder update method to improve the global search ability,and the elite individual firefly algorithm search strategy is combined to fully utilize the finder group information to improve the accuracy of the algorithm.In order to verify the algorithm performance of HSSA,a comparative experiment was conducted on 12 benchmark test functions with six population intelligent algorithms.The results showed that HSSA has higher optimization accuracy and faster convergence speed compared to the other six algorithms,and has consistent optimization performance in high-dimensional function functions.Statistical testing of the algorithm optimization results showed that there is a significant difference in the algorithm performance between HSSA and the six algorithms,HSSA has better algorithm performance.The effectiveness and practical effects of the two improved strategies are verified through comparative experiments on the optimization performance of the improved sparrow search algorithm incorporating a single improved strategy.Finally,HSSA is applied to practical optimization problems of UAV path planning and tension spring design,and experimental results verify the performance and practicality of HSSA algorithm.