An adaptive random reverse learning mayfly algorithm is proposed to overcome the shortcomings of mayfly optimization. Specifically, hyperbolic tangent function is used as adaptive curve to flexibly optimize the personal experience and social experience coefficients of the algorithm, so that the algorithm can better balance the global and local search capabilities through the evolutionary iterations. In addition, on the basis of elite mayfly in each generation, random reverse learning mechanism is used to expand population diversity and speed up algorithm convergency. The calculation results of several test functions show that the algorithm presented has good convergence accuracy and improved algorithm performance.
Aiming at the problems of sparrow algorithm (SSA), such as easy to fall into local extremum, uneven initial population distribution and slow convergence in late iteration, a sparrow search algorithm (CMSSA) was proposed, which combined cosine similarity and random multi-chaotic disturbance. The algorithm firstly integrates the reverse learning strategy and initializes the population by using the population uniform adjustment strategy of cosine similarity to ensure the uniformity and richness of the population, so that the algorithm can better search for the global optimal solution. Secondly, a random selection mechanism of multi-chaos local search strategy is used to take advantages from different disturbance states of multiple chaos models. Randomly selected chaos maps are used in each iteration to perturb individuals and help SSA get rid of local extremums. Simulation results show that compared with other intelligent algorithms, CMSSA achieves better results in robustness and optimization accuracy.
Harris hawk optimization algorithm (HHO) is one of the population-based algorithms proposed by in 2019. It has received great attention from researchers. However, HHO algorithm still has some problems, such as the exploitation ability is too large compared with the exploration ability, which leads to low optimization accuracy, slow convergence speed and so on. Therefore, the collaborative strategy and quantization strategy are introduced. With the support of the two strategies, the algorithm can avoid falling into local extremum in the early phase of iteration and improve its optimization accuracy at the late phase of iteration. Through the test of four representative functions, compared with the other three basic algorithms, the proposed algorithm greatly improves the optimization accuracy and convergence speed of the optimal solution, and the ability to find global extremum is greatly improved.
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