基于多自适应策略的鼠群优化算法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI:10.1109/CCIS53392.2021.9754632

Ziyue Xu, Xiaodan Liang, Maowei He, Hanning Chen

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引用次数: 3

摘要

鼠群优化器(RSO)是一种新型的基于群体智能的全局优化算法。它的主要思想是模拟老鼠追逐和对抗猎物的行为。提出了一种基于多自适应策略的改进RSO，称为MARSO。多种自适应策略包括自适应学习范例(ALE)和自适应群体大小(APS)。在本文中，通过比较几种经典或新颖的优化算法，在29个IEEE CEC2017函数上验证了MARSO的性能。实验结果表明，这两种策略都能使RSO获得更优异的性能。

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Multiple Adaptive Strategies-based Rat Swarm Optimizer

Rat Swarm Optimizer (RSO) is a novel Swarm-intelligence based algorithms for solving global optimization problems. Its main idea is simulating the behavior of rats chasing and fighting their prey. There is an improved RSO according to multiple adaptive strategies, named as MARSO, is proposed. The multiple adaptive strategies include adaptive learning exemplars (ALE) and adaptive population size (APS). In this paper, the performance of MARSO was validated on the 29 IEEE CEC2017 functions by comparing with several classic or novel optimization algorithms. The experimental results show these two strategies enable RSO to get more excellent performance.

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2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)

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