Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its Application

Abstract

In the multi-objective optimization algorithm, the parameter strategy has a huge impact on the performance of the algorithm, and it is difficult to set a set of parameters with excellent distribution and convergence performance in the actual optimization process. Based on the MOEA/D algorithm framework, this paper construct an improved dual-population co-evolution MOEA/D algorithm by adopt the idea of dual-population co-evolution. The simulation test of the benchmark functions shows that the proposed dual-population co-evolution MOEA/D algorithm have significant improvements in IGD and HV indicators compare with three other comparison algorithms. Finally, the application of the LTE base station power allocation model also verifies the effectiveness of the proposed algorithm.