Sci Rep. 2025 Aug 23;15(1):31012. doi: 10.1038/s41598-025-16539-8.
ABSTRACT
In multi-objective particle swarm optimization, achieving a balance between solution convergence and diversity remains a crucial challenge. To cope with this difficulty, this paper proposes a novel multi-objective particle swarm algorithm, called ASDMOPSO, which aims to improve the optimization efficiency through the angular division of the archive and the dynamic update strategy. The algorithm efficiently classifies non-dominated solutions by dividing the external archive region into equal angles, thus achieving fine management and diversity maintenance of solutions during the optimization process. When the external archive overflows, the algorithm removes the solution in the highest density region using the congestion distance metric. At the same time, the research presents a multi-stage initialization approach. This method splits the random population into two subpopulations. Subsequently, a genetic algorithm and a differential evolutionary algorithm are utilized for optimization purposes in each subpopulation, respectively. As a result, the quality of the initial population is enhanced. To explore the solution space more efficiently, this paper designs a dynamic flight parameter adjustment technique. This technique balances exploration and exploitation by adjusting the optimization algorithm parameters in real time. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions, and statistical tests, sensitivity analysis, and complexity analysis are conducted. The experimental results show that the ASDMOPSO algorithm is more competitive than other comparison algorithms, with significantly improved optimization efficiency. For example, on the ZDT4 test function, its average IGD value is 0.032, outperforming the standard PSO algorithm and surpassing all other comparison algorithms, thereby validating the algorithm’s superiority in complex multi-objective optimization problems.
PMID:40849576 | DOI:10.1038/s41598-025-16539-8
