In view of the shortcomings of whale optimization algorithm,such as easy to fall into local optimum and slow convergence speed,a simplex-guided whale optimization algorithm integrating historical memory was proposed.Firstly,in order to avoid the initial population being too concentrated and falling into the local optimum,the chaotic map was proposed to improve the initialized population and increase the population diversity.Secondly,in order to solve the problems of low convergence accuracy and slow convergence speed of the algorithm,a simplex guidance strategy fused with historical memory was proposed,and a virtual optimal solution was solved by using the simplex method and the constructed historical memory table as the guide in the next random search stage,which helped the population to conduct a more detailed search in the early exploration process.Finally,a new nonlinear parametric strategy is proposed to balance the development and exploration capabilities of the algorithm.The algorithm is applied to 12 typical complex function optimization problems,and compared with other five intelligent algorithms,the experimental results show that the improved algorithm is the first in terms of convergence accuracy and speed,so it can be shown that the algorithm has good global search ability and local development ability.
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