Peak Identification in Evolutionary Multimodal Optimization: Model, Algorithms, and Metrics.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Yu-Hui Zhang, Zi-Jia Wang
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引用次数: 0

Abstract

In this paper, we present a two-phase multimodal optimization model designed to efficiently and accurately identify multiple optima. The first phase employs a population-based search algorithm to locate potential optima, while the second phase introduces a novel peak identification (PI) procedure to filter out non-optimal solutions, ensuring that each identified solution represents a distinct optimum. This approach not only enhances the effectiveness of multimodal optimization but also addresses the issue of redundant solutions prevalent in existing algorithms. We propose two PI algorithms: HVPI, which uses a hill-valley approach to distinguish between optima, without requiring prior knowledge of niche radii; and HVPIC, which integrates HVPI with bisecting K-means clustering to reduce the number of fitness evaluations (FEs). The performance of these algorithms was evaluated using the F-measure, a comprehensive metric that accounts for both the accuracy and redundancy in the solution set. Extensive experiments on a suite of benchmark functions and engineering problems demonstrated that our proposed algorithms achieved a high precision and recall, significantly outperforming traditional methods.

进化多模态优化中的峰值识别:模型、算法和度量。
在本文中,我们提出了一个两阶段多模式优化模型,旨在高效、准确地识别多个最优解。第一阶段采用基于种群的搜索算法来定位潜在的最优方案,第二阶段则引入新颖的峰值识别(PI)程序来过滤非最优方案,确保每个识别出的方案都代表一个独特的最优方案。这种方法不仅提高了多模式优化的效率,还解决了现有算法中普遍存在的冗余解问题。我们提出了两种 PI 算法:HVPI 和 HVPIC,前者使用山谷法来区分最优解,而无需事先了解利基半径;后者将 HVPI 与 K-means 分叉聚类整合在一起,以减少适配性评估(FE)的数量。这些算法的性能是通过 F-measure 进行评估的,F-measure 是一个综合指标,同时考虑了解决方案集的准确性和冗余性。在一系列基准函数和工程问题上进行的广泛实验表明,我们提出的算法实现了较高的精确度和召回率,明显优于传统方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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