Improved osprey optimization algorithm based on multiple strategies and its application
Aiming at the problems of slow convergence speed,low convergence accuracy and easy to fall into local optimum of osprey optimization algorithm,an improved osprey optimization algorithm based on multi-strategy fu-sion was proposed.Firstly,the Cubic chaotic mapping is used to initialize the population to increase the diversity of the population.Secondly,the mutual benefit strategy is introduced to strengthen the information exchange between search individuals and improve the ability of the algorithm to jump out of the local optimum.Finally,the lens imag-ing reverse learning strategy is used to enhance the ability of the algorithm to jump out of the local optimum and bal-ance the exploration and exploitation of the algorithm.Through the comparative analysis and statistical test of 18 benchmark test functions,the algorithm has been significantly improved in convergence speed,optimization ac-curacy and stability.In addition,through the experimental comparison of pressure vessel design problems,the ap-plicability of the algorithm in practical engineering applications is further verified.
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