Whale optimization algorithm based on hybrid adaptive strategy
Dou Jin, Shengbing Chen, Anqi Lu, Feng Qiu · 2022
Whale optimization algorithm (WOA) is characterized by fewer parameters, simple structure, and stronger optimization seeking ability compared with traditional optimization algorithms, but in practical applications there are problems such as sluggish convergence speed and easily falling into local optimal solutions. This work proposes MAWOA, a whale optimization algorithm based on hybrid adaptive strategy, introducing a method to adaptively adjust the weights with the iterative situation of the population to accelerate the convergence of the algorithm; designing an adaptive adjustment threshold, and individuals select a random search method according to the value of the threshold to enhance the global search ability of the population and circumventing local values; introducing an adaptive nonlinear convergence factor to strengthen the algorithm in initial exploration breadth and later local development process. Twelve different morphological benchmark functions and a MAWOA-BP wine quality classification model were used to optimize the experiment. The results shows that MAWOA has stronger performance in terms of convergence speed and optimization-seeking accuracy, and the classification results are significantly improved compared with traditional classification models such as KNN, decision trees and BP neural networks.