CEGA: Research on Improved Multi-objective CE Optimization Algorithm

Abstract

Cross-Entropy (CE) algorithm was originally designed to solve the problem of rare event simulation, and later was promoted to solve the single-objective optimization problem. But this paper attempts to solve the multi-objective optimization problem by using the Cross-Entropy algorithm. As a new heuristic stochastic optimization algorithm, there is no need to optimize the problem with gradient but only adaptability. The cross-entropy optimization algorithm has attracted widespread academic attention due to its low computational complexity and high robustness. Aiming at the problem that the CE algorithm is efficient but the accuracy of solving some questions is still muted, a mixed CEGA algorithm is proposed. First, the characteristics of the NSGA-II algorithm are used to further improve the performance of the CE algorithm. Secondly, using the method of segmenting population size, more accurate results can be obtained with the same numbers of calculation times. Finally, by comparing 11 kinds of standard test functions with 5 kinds of classical multi-objective optimization algorithms, the improved algorithm shows higher accuracy and stronger global search capabilities than other original methods.