Improved Dragonfly Algorithm Based on Mixed Strategy

Abstract

With the rapid development of today, the swarm intelligence optimization algorithm is very popular and has been used in many fields. Dragonfly algorithm (DA) is one of the optimization algorithms, which has been used in some aspects. But it still has some shortcomings, such as slow convergence speed and search precision, and it is also prone to fall into local optimal solutions. Aiming at the shortcomings of the original dragonfly algorithm, a mixed strategy improved dragonfly algorithm (MSDA) is proposed. The algorithm introduces some improvement strategies. Firstly, initialize the population with the Sobol sequence. This allows the algorithm to obtain a better initial population, improve the quality of initial solution. Secondly, inertia weight improvement, use a nonlinear decreasing inertia weight. The modification of inertia weight makes the algorithm better adapt to the convergence process. Then using Cauchy mutation to increase the diversity of the population, improve the global search ability of the algorithm, and increase the search space. Finally, a random learning strategy is added. The random learning strategy enhances the diversity of the population, effectively improve the global optimization performance of the algorithm. The experiment is tested by eight standard test functions, results show that MSDA is better than the original DA algorithm in terms of convergence speed, solution accuracy and stability.