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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization
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Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization

机译：基于共生的替代学习多群粒子群算法

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摘要

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.
机译：受共生在自然生态系统中相互合作的想法启发，本文提出了一种新的PSO变体，即基于共生的替代学习多群粒子群优化（SALMPSO）。从中心位置，本地最佳位置和历史最佳位置（包括内部和外部多个群体的经验）中选择一个样本的学习概率被用来保持人口的多样性。提出了在多个群体之内和之间的两个不同层次的社会互动。在搜索过程中，粒子不仅与自己的子群中的其他人交流社交经验，而且还受到其他子群中粒子的经验的影响。根据不同的示例和学习策略，该模型被实例化为SALMPSO的四个变体，并进行了一组15个测试功能以与PSO的一些变体进行比较，分别包括10、30和50个维度。实验结果表明，在我们的仿真中，每种SALMPSO版本中的替代学习策略在收敛速度和大多数多峰函数的最优值方面都可以表现出更好的性能。

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