Archimedes Optimization Algorithm Fusing Aggregation Factor and Sine and Cosine Search
Aiming at the shortcomings of Archimedes optimization algorithm,which has poor convergence accuracy and weak ability to jump out of a local optimum,an improved Archimedes optimization algorithm YMAOA was proposed which combines aggregation factor and sine and cosine search.First,Sobol sequence was introduced to construct the initial population to enhance the diversity of population.Second,the density factor was reconstructed as a nonlinear decreasing trend,and the nonlinear weight was designed to balance the exploration ability and convergence speed of algorithm in different periods.Then,a random reverse learning strategy based on clustering factor judgment was designed to enhance the optimizing performance of global exploration.In the local optimization stage,the sine-cosine search was integrated to update the position,which helps the algorithm to jump away from a local optimum.The improved algorithm was compared with standard AOA and other same type of algorithms on nine benchmark functions.The results show that compared with similar improved AOA algorithms,YMAOA has advantages in both convergence speed and local optimal ability.Wilcoxon rank sum test results also prove YMAOA has significant advantages in search performance.
Archimedes optimization algorithmaggregation factorsine and cosine optimizationdensity factoropposite learning
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