Artificial Hummingbird Algorithm Based on Multi-strategy Improvement
To address the problems of insufficient global exploration capability and slow convergence of the artificial humming-bird algorithm(AHA)in the iterative process,a multi-strategy improved artificial hummingbird algorithm(IAHA)is proposed.Firstly,a strategy combining Tent chaos sequence and reverse learning is used to initialize the population,which generates high-quality initial populations and lays a foundation for global optimization of the algorithm.Secondly,the Levy flight strategy is in-troduced in the foraging stage to enhance the global search ability,enabling the algorithm to quickly escape from local optima and accelerate convergence speed.Finally,the simplex method is introduced into the algorithm to process poorer quality population be-fore each iteration ends,improving the local optimization ability of the algorithm.The IAHA is compared with 4 basic algorithms,3 single-improvement-stage artificial hummingbird algorithms,and 2 existing improved artificial hummingbird algorithms,respec-tively.Simulation experiments as well as Wilcoxon rank sum tests are performed on 8 benchmark test functions to evaluate the performance of IAHA and to analyze its time complexity.Experimental results show that IAHA converges faster,has better global optimization capability and better algorithmic performance than the above proposed algorithms.
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