Whale Optimization Algorithm Based on Chaotic Mapping and Dynamic Parameters

To tackle the shortcomings of the original WOA, including its sluggish convergence rate and tendency to approach local optima, an adaptive whale optimization algorithm (MWOA) combining chaotic maps and dynamic parameters is presented. First, a modified tent map was employed to create the whale population, increasing its diversity and distributing it more evenly. Then, to strengthen and balance the capabilities of global and local search, the adaptive weight coefficient is added and the linear convergence factor is modified. Finally, the findings of simulation trials on six test functions demonstrated that MWOA considerably increased convergence accuracy and speed.