Improved War Strategy Optimization Algorithm Based on Hybrid Strategy

The Standard WSO algorithm has several shortcomings, including uneven distribution of initial population, slow convergence speed, and weak global search ability. To address these issues, the present study proposes an improved War Strategy Optimization (WSO) based on hybrid strategy. To begin with, the initialization of the population was done using hypercube sampling. Additionally, diversification of the population during iteration process was achieved by adopting sine/cosine strategy, Cauthy mutation and backward learning strategy. Furthermore, to enhance capabilities in global search and local development, operator retention strategy from simulated annealing algorithm was employed. Finally, three test function optimization experiments were conducted which demonstrated that the proposed war strategy optimization algorithm based on hybrid strategy significantly improves both optimization accuracy and convergence speed.