Complex problems of the current business world need new approaches and new computational algorithms for solution. Majority of the issues need analysis from different angles, and hence, multi-objective solutions are more widely used. One of the recently well-accepted computational algorithms is Multi-objective Particle Swarm Optimization (MOPSO). This is an easily implemented and high time performance nature-inspired approach; however, the best solutions are not found for archiving, solution updating, and fast convergence problems faced in certain cases. This study investigates the previously proposed solutions for creating diversity in using MOPSO and proposes using random immigrants approach. Application of the proposed solution is tested in four different sets using Generational Distance, Spacing, Error Ratio, and Run Time performance measures. The achieved results are statistically tested against mutation-based diversity for all four performance metrics. Advantages of this new approach will support the metaheuristic researchers.

当前商业世界中的复杂问题需要新的方法和新的计算算法来解决。大多数问题需要从不同角度进行分析，因此，多目标解决方案得到了更广泛的应用。最近广为接受的计算算法之一是多目标粒子群优化（MOPSO）。这是一种易于实施且具有高性能的自然灵感方法。但是，在某些情况下，找不到用于归档，解决方案更新和快速收敛问题的最佳解决方案。这项研究调查了先前提出的使用MOPSO创造多样性的解决方案，并提出了使用随机移民的方法。使用世代距离，间距，错误率和运行时性能指标在四个不同的集合中测试了所提出解决方案的应用。针对所有四个性能指标，针对基于突变的多样性对获得的结果进行统计测试。这种新方法的优势将支持元启发式研究人员。