AHPS2: An optimizer using adaptive heterogeneous particle swarms
AHPS2: An optimizer using adaptive heterogeneous particle swarms
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AHPS2：使用自适应异构粒子群的优化器
DOI：
10.1016/j.ins.2014.04.043
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发表时间：
2014-10
影响因子：
8.1
通讯作者：
Qiang Lu
中科院分区：
文献类型：
--
作者：
Mengqi Hu;Teresa Wu;Jeffery D. Weir;Qiang Lu
粒子群优化算法（PSO）自诞生以来就存在早熟收敛和缺乏多样性等问题。多群粒子群算法（MS-PSO）是粒子群算法中的一个新兴的进步，它旨在增加群的多样性。然而，大多数MS-PSO是针对特定问题开发的，因此它们在不同景观上的搜索能力仍然不尽如人意。此外，到目前为止，MS-PSO的研究将子群视为具有最小竞争（如果不是没有竞争）的合作群体。此外，每个子群的大小被设置为固定的，这可能会遇到过多的计算成本。为了解决这些问题，一种新的优化器使用自适应异质粒子群（AHPS 2）在这项研究中开发。在AHPS 2中，引入了多个异质群，每个群由一组具有相似学习策略的同质粒子组成。研究了两种互补搜索技术：综合学习和次梯度法。为了更好地利用异构学习策略，提出了一种自适应竞争策略，使每个群体的规模可以根据其组性能动态调整。通过对群体异质性和竞争模型的分析，验证了该算法的有效性.此外，AHPS 2和最先进的算法之间的比较分为三类：36个常规基准函数（30维），20个大规模基准函数（1000维）和3个现实世界的问题。实验结果表明，AHPS 2在求解精度和统计检验方面均优于其他基于群体的算法或进化算法。
Particle swarm optimization (PSO) has suffered from premature convergence and lacked diversity for complex problems since its inception. An emerging advancement in PSO is multi-swarm PSO (MS-PSO) which is designed to increase the diversity of swarms. However, most MS-PSOs were developed for particular problems so their search capability on diverse landscapes is still less than satisfactory. Moreover, research on MS-PSO has so far treated the sub-swarms as cooperative groups with minimum competition (if not none). In addition, the size of each sub-swarm is set to be fixed which may encounter excessive computational cost. To address these issues, a novel optimizer using Adaptive Heterogeneous Particle SwarmS (AHPS2) is developed in this research. In AHPS2, multiple heterogeneous swarms, each consisting of a group of homogenous particles having similar learning strategy, are introduced. Two complementary search techniques, comprehensive learning and a subgradient method, are studied. To best take advantage of the heterogeneous learning strategies, an adaptive competition strategy is proposed so the size of each swarm can be dynamically adjusted based on its group performance. The analyses of the swarm heterogeneity and the competition models are presented to validate the effectiveness. Furthermore, comparisons between AHPS2and state-of-the-art algorithms are grouped into three categories: 36 regular benchmark functions (30-dimensional), 20 large-scale benchmark functions (1000-dimensional) and 3 real-world problems. Experimental results show that AHPS2displays a better or comparable performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.
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影响因子：
4.1
作者：
Matthieu Weber;V. Tirronen;Ferrante Neri
通讯作者：
Matthieu Weber;V. Tirronen;Ferrante Neri
DOI：
10.1016/j.asoc.2012.11.039
发表时间：
2013-04
期刊：
Appl. Soft Comput.
影响因子：
--
作者：
Giovanni Iacca;Fabio Caraffini;Ferrante Neri
通讯作者：
Giovanni Iacca;Fabio Caraffini;Ferrante Neri
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
10.1109/cec.2008.4631014
发表时间：
2008-06
期刊：
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
影响因子：
--
作者：
Zhenyu Yang;K. Tang;X. Yao
通讯作者：
Zhenyu Yang;K. Tang;X. Yao