Snake Optimize Based on Elite Reverse Learning and Sine-Cosine Algorithm Optmization
A Snake Optimizer(ESSO)based on elite reverse learning strategy and sine-cosine algorithm optimi-zation is proposed to solve the problems of low solution accuracy and slow convergence speed of Snake Optimizer.First,when initializing the population,the elite reverse learing strategy is introduced to generate the initial population,in order to increase the diversity of the initial individuals;In the combat mode,the sine and cosine algorithm is intro-duced into the position update formula of male and female individuals,which effectively avoids fallinginto local opti-mum,and introduces adaptive weights to balance global andlocal search capabilities;Finally,performance tests are performed based on 12 benchmark functions toevaluate the efficiency of theimproved algorithm.The results show that compared with the other six algorithms,the improved algorithmhas better global search ability and solution robustness.At the same time,the optimization accuracy andconvergence speed of the algorithm are also better than the previous Snake Optimizer.
Snake OptimizerReverse learningSine and cosine algorithm
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