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In order to improve the CSA with low speed of convergence and insufficient convergence accuracy. This paper proposed a MSCSA. Firstly, the algorithm introduced an adaptive weight coefficient with disturbance of tent sequence at early stage which could improve the convergence speed of the algorithm. Secondly, the algorithm introduced an operator which mixed by golden sine and moth-flame at late stage. At last, the algorithm improved the discovery probability AP which could improve the convergence accuracy of the algorithm by increasing the randomness. This paper determined the value of the iteration coefficient by comparing and testing on 9 benchmark functions. The paper verified the performance of the algorithm by the Wilcoxon rank sum test. The results of the test prove that the performance of MSCSA is better. [ABSTRACT FROM AUTHOR]

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 defects that the GOA is easy to fall into the local optimal solution and the convergence speed is slow, this paper proposed a Fuch chaotic grasshopper algorithm based on pigeon swarm algorithm( PFGOA). Firstly, this paper introduced the Fuch chaotic map to generate initialized population. And then it embedded the sine-cosine operator into the updating formula of the position. At the same time, it introduced pigeon search operator to the optimization process, and through changing the inertia weight c to improve and balance the ability of exploration and development of the algorithm. Finally, it introduced the nonlinear inertia weight to the optimal individual to increase the global search ability of the algorithm, and jumped out of local optimization. Through the simulation test of 11 test functions and Wilcoxon rank sum test, the experimental simulation results show that the PFGOA has better performance, high er precision and better significance . [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]

This paper proposed a hybrid flower pollination algorithm (refrHFPA) based on refraction principle for the slow convergence rate of flower pollination algorithm and low optimization accuracy. The algorithm firstly used the harmony search algorithm to improve the convergence speed of the algorithm, then it used the refraction principle to improve the diversity of the population, and helped the algorithm to jump out of the local optimal and improved the accuracy of optimization. It used eight test functions to compare other intelligent algorithms, and the results show that the refrHFPA algorithm has a significant improvement in convergence speed and optimization accuracy. [ABSTRACT FROM AUTHOR]