A new Algorithm based on the Gbest of Particle Swarm Optimization algorithm to improve Estimation of Distribution Algorithm

In recent years, with the rise of artificial intelligence and deep learning, as an evolutionary algorithm based on probability model, estimation of distribution algorithm has been widely research and development. The estimation of distribution algorithm without the traditional genetic operation such as crossover and mutation, is a new kind of evolution model. As an algorithm based on probabilistic mode, the estimation of distribution algorithm establishes a probabilistic model describing the solution space of optimization problems. With the emergence for big data, the convergence of the algorithm and the requirements for solving precision are also increasing. This paper attempts to improve the distribution estimation algorithm. The optimal population of each iteration is found through the location update of each iteration of the Particle Swarm Optimization (PSO) algorithm. The simulation test was carried out with ten benchmark test function. The proposed algorithm was compared with the GA_EDA9improved genetic algorithm) and the basic distribution estimation (EDA) algorithm. Experimental results show that the new algorithm is superior to GA_EDA and basic EDA in terms of convergence and accuracy.