| Abstrakt: |
Firstly, according to the particle structure information in the muiti-objective particle swarm opiimzzaiion algorithm, using nondominated soluiion sets to construct the topological structure between individual particle neighborhoods, a star-structured muiti-objective particle swarm opiimizaiion algorithm is proposed for solving multi-modal multi-objective problems. Secondly, in view of the difficulty of selecting the global optimal individual in the muiti-objective particle swarm, an evaluation method for the uniformity of the distribution of non-dominated solution sets is proposed. The evaluation result determines the global optimal individual corresponding to the current particle. Finally, combining two methods, a star topology multi-objective particle swarm optimization algorithm with uniform calculation method is proposed. The test function analyzes the convergence of the algorithm and shows that the improved algorithm converges faster than the original algorithm. Experimental results show that the algorithm can take into account the distribution of the problem object space and decision space, and effectively solve the multi-modal multi-objective problem. [ABSTRACT FROM AUTHOR] |
