A modified sea-horse optimizer with a hybrid sine cosine algorithm
The sea-horse optimizer(SHO)is a meta-heuristic algorithm that simulates the movement,preda-tion,and reproduction behavior of seahorses.It offers the benefits of minimal control parameters and effort-less deployment.To address the limitations of the SHO in complex scenarios,such as slow convergence speed and easy falling into the local optimal,a modified algorithm of hybrid sine cosine algorithm and sea-horse op-timizer,called HSCASHO,is proposed in this paper.Firstly,the mathematical model of the SHO is modified by parameter adjustment to better balance global exploration and local exploitation;Secondly,a weight based on fitness value is introduced,and a new search formula is designed for the original sine cosine algorithm to accelerate the convergence speed;Finally,the process of variation enhances population diversity and helps the algorithm to escape the local optimal.Combining the advantages of both algorithms,HSCASHO uses a pa-rameter-tuned SHO for the first 3/4 iterations and a sine cosine algorithm that updates the search formula for the last 1/4 iterations.Experiments comparing the HSCASHO algorithm with the four algorithms on 10 bench-mark functions show that the algorithm significantly outperforms the other algorithms.
sea-horse optimizersine cosine algorithmparameter adjustmentvariation process
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