A segmented multi-objective CE optimization algorithm

In order to solve the multi-objective optimization problem, this paper proposes a multi-objective cross-entropy (CE) optimization algorithm based on the original single target cross-entropy optimization algorithm. Considering that the CE algorithm is easy to fall into the local optimal solution, as well as the algorithm's search ability is not strong enough, continue to propose a method of segmenting population size, and the effectiveness of this method is proved by experiments. This improved algorithm is named as s-MOCE. At the same time, the individual selection mechanism is adopted to retain the elite individuals in the evolution process and extracts the distribution information through the elite retention strategy to constantly correct the parameters of the normal distribution probability model of the algorithm. Comparing the improved CE algorithm in terms of HV (hyper-volume) index with other well-known algorithms like NSGA-II, dMOPSO, MOEA/D and PAES using the 9 kinds of standard test functions WFG1-WFG9. The results prove that the algorithm proposed in this paper is superior to other algorithms. It has the advantages of fast convergence speed, strong searching ability, and high robustness.