Whale Optimization Algorithm Based on Dynamic Inertia Weight Factor

Aiming at the drawbacks of whale optimization algorithm (WOA) that the precision is poor, the convergence speed is slow, and it is easily trapped in local optimization when solving complex projects, this paper proposes an improved Whale Optimization Algorithm (IWOA).This strategy incorporates dynamic inertia weight factor into the updated expression of the traditional whale position to guarantee a counterbalance between the early and late algorithm’s ability to search for an optimal position, thus improving the search precision and rate of the WOA. In addition, for validating the effectiveness of our proposed algorithm in dealing with multidimensional and nonlinear optimization problems, six classical benchmark functions are selected as the test objective in this paper. The test functions contain unimodal and multimodal functions of fixed dimensions and are compared with genetic algorithm (GA), particle swarm algorithm (PSO) and standard whale optimization algorithm. The test results show that the improved algorithm converges faster and is more accurate than the original algorithm and several classical algorithms. And it has better results in the values of the two indicators, mean and standard deviation.