Example-based learning particle swarm optimization for continuous optimization
Example-based learning particle swarm optimization for continuous optimization
复制标题
基于实例的学习粒子群优化以实现持续优化
DOI：
10.1016/j.ins.2010.10.018
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发表时间：
2012
影响因子：
8.1
通讯作者：
黄翰
中科院分区：
文献类型：
--
作者：
林良才;秦虎;郝志峰;黄翰
粒子群优化（PSO）是一种基于群体智能的启发式优化技术，其灵感来自于鸟类的群集行为。标准粒子群算法存在早熟收敛的缺点。几种改进的粒子群算法在搜索过程中很好地保持了粒子的多样性，但收敛速度较慢。本文提出了一种基于示例的学习PSO（ELPSO），通过保持群体多样性和收敛速度之间的平衡来克服这些缺点。受多个好例子可以引导人群取得进步的社会现象的启发，ELPSO使用多个全局最佳粒子的例子集来更新粒子的位置。在这项研究中，样本集的粒子是从最好的粒子中选择的，并在每次迭代中以先进先出的顺序由更好的粒子更新。示例集中的粒子各不相同，并且就目标优化函数而言通常质量较高。数学和数值结果证明了ELPSO算法比单最优和非最优PSO算法具有更好的多样性和收敛速度。最后，对基准问题的计算实验表明，ELPSO优于所有的测试粒子群算法的解决方案的质量和收敛时间。
Particle swarm optimization (PSO) is a heuristic optimization technique based on swarm intelligence that is inspired by the behavior of bird flocking. The canonical PSO has the disadvantage of premature convergence. Several improved PSO versions do well in keeping the diversity of the particles during the searching process, but at the expense of rapid convergence. This paper proposes an example-based learning PSO (ELPSO) to overcome these shortcomings by keeping a balance between swarm diversity and convergence speed. Inspired by a social phenomenon that multiple good examples can guide a crowd towards making progress, ELPSO uses an example set of multiple global best particles to update the positions of the particles. In this study, the particles of the example set were selected from the best particles and updated by the better particles in the first-in-first-out order in each iteration. The particles in the example set are different, and are usually of high quality in terms of the target optimization function. ELPSO has better diversity and convergence speed than single-gbest and non-gbest PSO algorithms, which is proved by mathematical and numerical results. Finally, computational experiments on benchmark problems show that ELPSO outperforms all of the tested PSO algorithms in terms of both solution quality and convergence time.
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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：
10.1201/9781003206477-5
发表时间：
2021-08
期刊：
Evolutionary Optimization Algorithms
影响因子：
--
作者：
A. Badar
通讯作者：
A. Badar
DOI：
10.4028/www.scientific.net/amr.850-851.809
发表时间：
2013-12
期刊：
Advanced Materials Research
影响因子：
--
作者：
H. Ni;Wei Wang
通讯作者：
H. Ni;Wei Wang
DOI：
10.1201/9780429422614-20
发表时间：
2018-10
期刊：
Swarm Intelligence Algorithms
影响因子：
--
作者：
Adam Slowik
通讯作者：
Adam Slowik