An Enhanced Whale Optimization Algorithm with Simplex Method

摘要

This paper proposes an enhanced whale optimization algorithm with the simplex method named SMWOA algorithm. SMWOA make WOA faster, more robust, and avoid premature convergence. The simplex method (SM) it-eratively optimizes the current worst step size, avoids the population search at the edge, and improves the convergence accuracy and speed of the algorithm. The SMWOA algorithm is compared with other well-known meta-heuristic algorithms on 5 benchmarks and 1 classical engineering design problem. The experimental results show that the SMWOA algorithm has better performance than other meta-heuristic optimization algorithms in low and high dimensions.