基于多种群动态变化的种群停滞检测与预防新方法

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Krystian Łapa, Danuta Rutkowska, Aleksander Byrski, Christian Napoli
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引用次数: 0

摘要

摘要本文在分析评价函数的局部改进和无限脉冲响应滤波器的基础上,提出了一种新的种群停滞检测机制。该机制的目的是提高各种优化场景下的种群停滞检测能力,从而提高基于多种群的算法(multi-population based algorithms, mpba)的性能。此外,已经提出了各种其他方法来消除停滞,包括旨在提高性能和降低算法复杂性的方法。开发的方法在各种迁移拓扑和各种mpba中进行了测试,其中包括MNIA算法,该算法允许使用许多不同的基本算法,从而消除了为给定仿真问题选择基于种群的算法的需要。对典型的基准函数和控制问题进行了仿真。所得结果证实了所建方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms
Abstract In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method.
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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