Enhancing Algorithm Selection through Comprehensive Performance Evaluation: Statistical Analysis of Stochastic Algorithms

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-11-16 DOI:10.3390/computation11110231
Azad Arif Hama Amin, Aso M. Aladdin, Dler O. Hasan, Soran R. Mohammed-Taha, Tarik Ahmed Rashid
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Abstract

Analyzing stochastic algorithms for comprehensive performance and comparison across diverse contexts is essential. By evaluating and adjusting algorithm effectiveness across a wide spectrum of test functions, including both classical benchmarks and CEC-C06 2019 conference functions, distinct patterns of performance emerge. In specific situations, underscoring the importance of choosing algorithms contextually. Additionally, researchers have encountered a critical issue by employing a statistical model randomly to determine significance values without conducting other studies to select a specific model for evaluating performance outcomes. To address this concern, this study employs rigorous statistical testing to underscore substantial performance variations between pairs of algorithms, thereby emphasizing the pivotal role of statistical significance in comparative analysis. It also yields valuable insights into the suitability of algorithms for various optimization challenges, providing professionals with information to make informed decisions. This is achieved by pinpointing algorithm pairs with favorable statistical distributions, facilitating practical algorithm selection. The study encompasses multiple nonparametric statistical hypothesis models, such as the Wilcoxon rank-sum test, single-factor analysis, and two-factor ANOVA tests. This thorough evaluation enhances our grasp of algorithm performance across various evaluation criteria. Notably, the research addresses discrepancies in previous statistical test findings in algorithm comparisons, enhancing result reliability in the later research. The results proved that there are differences in significance results, as seen in examples like Leo versus the FDO, the DA versus the WOA, and so on. It highlights the need to tailor test models to specific scenarios, as p-value outcomes differ among various tests within the same algorithm pair.
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通过综合性能评估加强算法选择:随机算法的统计分析
分析随机算法的综合性能并在不同环境下进行比较至关重要。通过在广泛的测试功能(包括经典基准和 CEC-C06 2019 会议功能)范围内评估和调整算法的有效性,可以发现不同的性能模式。在特定情况下,强调了根据具体情况选择算法的重要性。此外,研究人员还遇到了一个关键问题,即在没有进行其他研究以选择特定模型来评估性能结果的情况下,随意采用统计模型来确定显著性值。为了解决这个问题,本研究采用了严格的统计测试,以强调成对算法之间的实质性性能差异,从而强调统计显著性在比较分析中的关键作用。该研究还就各种算法对各种优化挑战的适用性提出了有价值的见解,为专业人员做出明智决策提供了信息。通过精确定位具有有利统计分布的算法对,可促进实际算法的选择。这项研究包含多种非参数统计假设模型,如 Wilcoxon 秩和检验、单因素分析和双因素方差分析检验。这种全面的评估增强了我们对不同评估标准下算法性能的把握。值得注意的是,该研究解决了以往算法比较中统计检验结果的差异,提高了后期研究结果的可靠性。结果证明,显著性结果存在差异,例如利奥算法与 FDO 算法、DA 算法与 WOA 算法等。由于同一算法对中各种测试的 p 值结果不同,这突出表明有必要根据具体情况调整测试模型。
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
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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