A Novel Whale Optimization Algorithm with Sparrow algorithm and Golden Sine Leading Strategy

Abstract

Whale optimization algorithm (WOA) is a recently proposed optimization algorithm. In view of the slow convergence velocity, low precision and hard to get away from local optimum of WOA algorithm, this paper puts forward a whale optimization algorithm with sparrow algorithm and golden sine leading strategy (SGSWOA). First, the location update rule of the producer in the sparrow algorithm is integrated into the Encircling prey stage of WOA to increase the search space of the algorithm and escape from the local optimum. Then combined with the golden sine leading strategy, it can balance exploration and development capabilities and enhance the performance of WOA algorithm. Finally, by optimizing 16 benchmark functions and applying the SGSWOA algorithm to practical engineering optimization problems, the experimental results display the SGSWOA algorithm has better convergence accuracy, convergence speed, and robustness.