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Aiming at the shortcomings of aquila optimization algorithm（AO）, this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation; Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm; Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. Through the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem, The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of COA, such as slow convergence speed, low solution accuracy and being easy to fall into local optimum, this paper proposed an improved coyote optimization algorithm based on dual strategy learning mechanism and adaptive chaotic mutation strategy ( DCSCOA ). Firstly, it adopted an oscillatory decline factor to generate diverse individuals for enhancing the global search ability. Secondly, it proposed a dual strategy learning mechanism to appropriately increase the influence of the group head wolf, so as to balance the local search ability and global search ability of the algorithm, and to improve the solution accuracy and convergence speed of the algorithm. Finally,it used an adaptive chaotic mutation mechanism to generate new individuals when the algorithm stagnates, so as to make the algorithm jump out of the local optimum. Through simulation experiments on 20 basic test functions and 11 CEC2017 test functions, the results show that the improved algorithm has higher solution accuracy,faster convergence speed and stronger stability. [ABSTRACT FROM AUTHOR]

In order to solve the disadvantage of the traditional whale optimization algorithm, which is slow convergence and easy to fall into local optimum, this paper proposed a mixed strategy based whale optimization algorithm. Firstly, it introduced the nonlinear adjustment strategy to modify the convergence factor, balanced the exploration and exploitation capability and accelerated the convergence speed. Then, it introduced an adaptive weighted coefficient into the position update formula of whales to improve the search precision of the algorithm. Finally, it combined the limit threshold idea of artificial bee colony algorithm to effectively jump out of the local optimum and prevent premature convergence. The results show that the proposed algorithm has better search precision and convergence speed through experiments on different dimensions of 14 benchmark functions. [ABSTRACT FROM AUTHOR]