The self-organizing migrating algorithm is a population-based algorithm belonging to swarm intelligence, which has been successfully applied in several areas for solving non-trivial optimization problems. However, based on our experiments, the original formulation of this algorithm suffers with some shortcomings as loss of population diversity, premature convergence, and the necessity of the control parameters hand-tuning. The main contribution of this paper is the development of the novel algorithm mitigating the mentioned issues of the original self-organizing migrating algorithm. We have applied the ideas of the self-adaptation of the control parameters, the different principle of the leader creation, and the external archive of the successful particles. For some special cases, we are able to utilize the differential grouping to detect the interacting variables effectively removing the need for the perturbation parameter. To prove the efficiency of the novel algorithm, we have performed experiments on fifteen unconstrained problems from the CEC 2015 benchmark. The algorithm is compared with seven well-known evolutionary and swarm algorithms. The results of the experiments indicate that the mechanisms used in the novel algorithm had significantly improved the performance of the original self-organizing migrating algorithm, and the new algorithm can now compete with the selected algorithms.

自组织迁移算法是属于群体智能的基于人口的算法，已成功应用于解决非平凡优化问题的多个领域。但是，根据我们的实验，该算法的原始公式存在一些缺点，如种群多样性的丧失，过早的收敛以及对控制参数进行手动调整的必要性。本文的主要贡献是开发了新颖的算法，从而减轻了原始自组织迁移算法的上述问题。我们应用了控制参数的自适应，领导者创建的不同原理以及成功粒子的外部存档的思想。对于某些特殊情况，我们能够利用差分分组来检测相互作用的变量，从而有效地消除了对扰动参数的需求。为了证明新算法的效率，我们从CEC 2015基准测试了15个不受约束的问题进行了实验。该算法与七个著名的进化算法和群体算法进行了比较。实验结果表明，新算法中使用的机制显着提高了原始自组织迁移算法的性能，新算法现在可以与所选算法竞争。该算法与七个著名的进化算法和群体算法进行了比较。实验结果表明，新算法中使用的机制显着提高了原始自组织迁移算法的性能，新算法现在可以与所选算法竞争。该算法与七个著名的进化算法和群体算法进行了比较。实验结果表明，新算法中使用的机制显着提高了原始自组织迁移算法的性能，新算法现在可以与所选算法竞争。