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

M. Zambrano-Bigiarini, M. Clerc, Rodrigo Rojas-Mujica
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引用次数: 310

Abstract

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 SPSO2011 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.
2011年CEC-2013标准粒子群优化:未来粒子群优化的基线
在这项工作中,我们首次将最新的标准粒子群优化算法(SPSO-2011)与为CEC-2013实参数单目标优化特别会议设计的28个测试函数进行了基准测试。SPSO-2011是对以前的PSO版本的重大改进,具有自适应随机拓扑和旋转不变性构成了主要的进步。结果表明,SPSO-2011在单峰和可分离测试函数族中表现优异,收敛到全局最优的速度快,而在四个旋转多模态测试函数族中表现良好。相反,SPSO-2011在所有组成问题(即专门为该竞赛设计的高度复杂的函数)和某些多模态测试函数上表现最差。总体而言,该算法能够快速收敛到全局最优区域,所需的函数评估少于10E+03次。然而,对于大多数组成函数和多模态函数,SPSO2011显示出有限的从次优区域“逃逸”的能力。尽管存在这些限制,SPSO-2011的一个理想特性是它的可扩展行为,它可以观察到多达50维的问题,即在不需要增加种群大小的情况下保持跨维度的相似性能。因此,未来粒子群算法的改进应该集中在增强算法解决不可分离和不对称函数的能力上,因为大量的局部极小值和第二个全局极小值离真正的最优值很远。这项工作是为公平比较未来PSO改进提供基线的第一次努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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