Social Emotional Optimization Algorithm Mixed Strategy

Social emotional optimization algorithm draws experience from social-emotional sociology, social psychology, organizational behavior theory and other academic achievements related to the formation, evolution, decision-making and other aspects of human groups, and also takes into account the role emotion plays in decision-making and the impact outside information has on human emotion, where human social behavior has been simulated. Each individual part of the algorithm represents a virtual person and all individuals the human society. In this virtual society, each individual tries to strike for a better living environment, social recognition, as well as higher social evaluation.In Social emotional optimization algorithm, individuals adjust itself according to the best social evaluation value other than the other feed-back information, which restricts the diversity of the algorithm: It is difficult to be further optimized around local extreme points and easy to fall into local minimum. To make a change, this essay introduces Metropolis Rules and considers the feed-back information from both sides to let individuals to accept not only the best society evaluation value, but also a certain probability of other values, which improves the diversity and the global search capacity.As a stochastic optimization algorithm, the local search capacity of Social emotional optimization algorithm needs to be improved. To achieve this, the essay brings into Quadratic Interpolation. In each iteration, the predicted extreme points will be used to update the local search efficiency and thus speed up the convergence.To demonstrate the optimize performance of improved algorithm ,we apply Social emotional optimization algorithm, Social emotional optimization algorithm with Metropolis rule and Social emotional optimization algorithm with Quadratic interpolation into solving the problems of nonlinear equations.The effect of improved algorithm is proved by the excellent performance of optimized algorithm.