To address the problems that the basic APO algorithm tends to fall into local optimum and poor distribution, this paper proposed a PEMOAPO algorithm. Firstly, this paper used the strategy of tent mapping and reverse learning to initialize the population and enhance the population diversity. Secondly, it introduced a mechanism of partition-guided individuals for evolution, and it adopted different mass functions and virtual force calculation rules to iteratively update the individuals in the feasible and infeasible domains to enhance the convergence performance of the algorithm. Choosing MW series and C_DTLZ series as the benchmark test functions in simulation experiments, and the comprehensive performance evaluation index comparison analysis, statistical analysis, convergence analysis and time complexity analysis show that the improved algorithm has good diversity and convergence, and can converge to the Pareto front quickly.

[2022-11-25] Accepted Paper /doc/2022.10.0465/v1

[2023-05-05] Printed Article /doc/2022.10.0465/v2