This paper presents an algorithm based on Particle Swarm Optimization (PSO), adapted for multi-objective optimization problems: the Elitist PSO (MO-ETPSO). The proposed algorithm integrates core strategies from the well-established NSGA-II approach, such as the Crowding Distance Algorithm, while leveraging the advantages of Swarm Intelligence in terms of individual and social cognition. A novel aspect of the algorithm is the introduction of a swarm memory and swarm elitism, which may turn the adoption of NSGA-II strategies in PSO. These features enhance the algorithm's ability to retain and utilize high-quality solutions throughout optimization. Furthermore, all operators within the algorithm are intentionally designed for simplicity, ensuring ease of replication and implementation in various settings. Preliminary comparisons with the NSGA-II algorithm for the Green Vehicle Routing Problem, both in terms of solutions found and convergence, have yielded promising results in favor of MO-ETPSO.

翻译：本文提出了一种基于粒子群优化算法（PSO）的多目标优化改进算法：精英粒子群优化算法（MO-ETPSO）。该算法融合了成熟NSGA-II方法的核心策略（如拥挤距离算法），同时利用群体智能在个体认知与社会认知方面的优势。其创新之处在于引入群体记忆与群体精英机制，使NSGA-II策略得以在PSO框架中实现。这些特性增强了算法在优化过程中保持和利用高质量解的能力。此外，算法中的所有算子均经过简化设计，便于在不同场景下复现与实现。针对绿色车辆路径问题的初步对比实验表明，MO-ETPSO在解的质量与收敛性方面均优于NSGA-II算法。