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Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements

机译:CEC-2013的2011年标准粒子群优化:未来PSO改进的基准

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In this work we benchmark, for the first time, the latest Standard Particle Swarm Optimisation algorithm (SPSO-2011) against the 28 test functions designed for the Special Session on Real-Parameter Single Objective Optimisation at CEC-2013. SPSO-2011 is a major improvement over previous PSO versions, with an adaptive random topology and rotational invariance constituting the main advancements. Results showed an outstanding performance of SPSO-2011 for the family of unimodal and separable test functions, with a fast convergence to the global optimum, while good performance was observed for four rotated multimodal functions. Conversely, SPSO-2011 showed the weakest performance for all composition problems (i.e. highly complex functions specially designed for this competition) and certain multimodal test functions. In general, a fast convergence towards the region of the global optimum was achieved, requiring less than 10E+03 function evaluations. However, for most composition and multimodal functions SPSO-2011 showed a limited capability to “escape” from sub-optimal regions. Despite this limitation, a desirable feature of SPSO-2011 was its scalable behaviour, which observed up to 50-dimensional problems, i.e. keeping a similar performance across dimensions with no need for increasing the population size. Therefore, it seems advisable that future PSO improvements be focused on enhancing the algorithm's ability to solve non-separable and asymmetrical functions, with a large number of local minima and a second global minimum located far from the true optimum. This work is the first effort towards providing a baseline for a fair comparison of future PSO improvements.
机译:在这项工作中,我们首次针对在CEC-2013上的实参单目标优化特别会议上设计的28个测试函数,对最新的标准粒子群优化算法(SPSO-2011)进行了基准测试。 SPSO-2011是对先前PSO版本的一项重大改进,具有自适应随机拓扑和旋转不变性是主要的进步。结果表明,SPSO-2011在单峰和可分离测试功能系列中具有出色的性能,可以快速收敛到全局最优值,而在四个旋转的多峰功能上则观察到了良好的性能。相反,SPSO-2011在所有成分问题(即专门为该比赛设计的高度复杂的功能)和某些多峰测试功能方面的表现最差。通常,实现了向全局最佳区域的快速收敛,需要少于10E + 03的功能评估。但是,对于大多数组合功能和多模式功能,SPSO-2011表现出从次优区域“逃脱”的能力有限。尽管有此限制,但SPSO-2011的一项理想功能是其可扩展的行为,该行为可观察到多达50个维度的问题,即在各个维度上保持类似的性能,而无需增加总体规模。因此,建议未来的PSO改进集中在增强算法解决不可分离和不对称函数的能力上,因为存在大量的局部最小值和远离真实最优值的第二个全局最小值。这项工作是为公平比较未来PSO改进提供基准的第一步。

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