The performance of individual evolutionary optimization algorithms is mostly measured in terms of statistics such as mean, median and standard deviation etc., computed over the best solutions obtained with few trails of the algorithm. To compare the performance of two algorithms, the values of these statistics are compared instead of comparing the solutions directly. This kind of comparison lacks direct comparison of solutions obtained with different algorithms. For instance, the comparison of best solutions (or worst solution) of two algorithms simply not possible. Moreover, ranking of algorithms is mostly done in terms of solution quality only, despite the fact that the convergence of algorithm is also an important factor. In this paper, a direct comparison approach is proposed to analyze the performance of evolutionary optimization algorithms. A direct comparison matrix called \emph{Prasatul Matrix} is prepared, which accounts direct comparison outcome of best solutions obtained with two algorithms for a specific number of trials. Five different performance measures are designed based on the prasatul matrix to evaluate the performance of algorithms in terms of Optimality and Comparability of solutions. These scores are utilized to develop a score-driven approach for comparing performance of multiple algorithms as well as for ranking both in the grounds of solution quality and convergence analysis. Proposed approach is analyzed with six evolutionary optimization algorithms on 25 benchmark functions. A non-parametric statistical analysis, namely Wilcoxon paired sum-rank test is also performed to verify the outcomes of proposed direct comparison approach.

Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the objective is to either maximize the connection within the community or minimize connections between the communities. To apply NIOAs, either of the two, or both objectives are explored. Since NIOAs mostly exploit randomness in their strategies, it is necessary to analyze their performance for specific applications. In this paper, NIOAs are analyzed on the community detection problem. A direct comparison approach is followed to perform pairwise comparison of NIOAs. The performance is measured in terms of five scores designed based on prasatul matrix and also with average isolability. Three widely used real-world social networks and four NIOAs are considered for analyzing the quality of communities generated by NIOAs.

监督机器学习（ML）模型的性能比较是根据在测试数据集中获得的基于混淆矩阵的分数广泛进行的。但是，数据集包含多个具有不同难度级别的实例。因此，比较ML模型对单个实例的有效性，而不是比较整个数据集获得的分数更为合乎逻辑。在本文中，提出了一种替代方法，以根据数据集中的各个实例直接比较监督的ML模型。引入了一个直接比较矩阵，称为\ emph {prayatul矩阵}，该矩阵在数据集的不同实例上说明了两种ML算法的比较结果。根据Prayatul矩阵设计了五种不同的性能措施。在三个数据集上使用四种分类技术分析了所提出的方法以及设计措施的功效。还对四个大型复杂图像数据集进行了分析，该数据集使用四个深度学习模型，即Resnet50v2，MobilenetV2，EdgitionNet和XceptionNet。结果很明显，新设计的度量能够对比较ML算法有更多的了解，而现有基于混淆矩阵（如精度，精度和回忆）的分数是不可能的。

排名汇总旨在将许多替代品的偏好排名与不同选民的偏替排名组合成单一共识排名。然而，作为各种实际应用的有用模型，它是一个计算上有挑战性的问题。在本文中，我们提出了一种有效的混合进化排名算法来解决完整和部分排名的排名聚集问题。该算法具有基于协调对的语义交叉，并通过有效的增量评估技术加强了较晚的验收本地搜索。进行实验以评估算法，与最先进的算法相比，表明基准实例上具有高度竞争性能。为了展示其实际有用性，算法应用于标签排名，这是一个重要的机器学习任务。

通常，通过比较使用不同算法获得的社区的评估度量值来评估社区检测算法。用于衡量社区质量的评估指标结合了实体的拓扑信息，例如社区内部或外部节点的连接性。但是，在比较度量值的同时，它失去了社区拓扑信息在比较过程中的直接参与。在本文中，提出了一种直接比较方法，直接比较了两种算法获得的社区的拓扑信息。质量度量是基于社区拓扑信息的直接比较而设计的。考虑到新设计的质量度量，开发了两个排名方案。研究了八种广泛使用的现实世界数据集和六种社区检测算法的拟议质量指标以及排名方案的功效。

多模式的多目标问题（MMOP）通常在现实世界中出现，而决策空间中遥远的解决方案对应于非常相似的目标值。为了获得MMOP的所有溶液，已经提出了许多多模式多模式的多模型进化算法（MMEAS）。目前，很少有研究涵盖了最近提出的大多数代表性MMEAS，并进行了比较。在这项研究中，我们首先回顾了过去二十年中相关作品。然后，我们选择了12种利用不同多样性维护技术的最先进的算法，并比较了它们在现有测试套件上的性能。实验结果表明，不同类型的MMOP上不同技术的优势和劣势，从而为如何在特定情况下选择/设计MMEAS提供指导。

Non-parametric tests can determine the better of two stochastic optimization algorithms when benchmarking results are ordinal, like the final fitness values of multiple trials. For many benchmarks, however, a trial can also terminate once it reaches a pre-specified target value. When only some trials reach the target value, two variables characterize a trial's outcome: the time it takes to reach the target value (or not) and its final fitness value. This paper describes a simple way to impose linear order on this two-variable trial data set so that traditional non-parametric methods can determine the better algorithm when neither dominates. We illustrate the method with the Mann-Whitney U-test. A simulation demonstrates that U-scores are much more effective than dominance when tasked with identifying the better of two algorithms. We test U-scores by having them determine the winners of the CEC 2022 Special Session and Competition on Real-Parameter Numerical Optimization.

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change in the network occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can better keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.

经验丰富的用户通常在解决现实世界优化问题方面具有有用的知识和直觉。用户知识可以作为可变关系的配方，以帮助优化算法更快地找到良好的解决方案。此类间相互作用也可以自动从优化运行中的中间迭代中发现的高性能解决方案中自动学习 - 一种称为Innovization的过程。如果用户对这些关系进行审查，则可以在新生成的解决方案中执行，以将优化算法引导到搜索空间中实际上有希望的区域。对于大规模问题，这种可变关系的数量可能很高，就会出现挑战。本文提出了一个基于交互式知识的进化多目标优化（IK-EMO）框架，该框架将隐藏的可变关系提取为从不断发展的高性能解决方案中的知识，与用户共享它们以接收反馈，并将其应用于优化提高其有效性的过程。知识提取过程使用系统而优雅的图形分析方法，该方法与变量数量很好地缩放。在三个大规模的现实世界工程设计问题上证明了拟议的IK-EMO的工作。提出的知识提取过程和高性能解决方案的实现的简单性和优雅迅速表明了所提出的框架的力量。提出的结果应激发进一步的基于相互作用的优化研究，以实践其常规使用。

Metaheuristics are popularly used in various fields, and they have attracted much attention in the scientific and industrial communities. In recent years, the number of new metaheuristic names has been continuously growing. Generally, the inventors attribute the novelties of these new algorithms to inspirations from either biology, human behaviors, physics, or other phenomena. In addition, these new algorithms, compared against basic versions of other metaheuristics using classical benchmark problems without shift/rotation, show competitive performances. In this study, we exhaustively tabulate more than 500 metaheuristics. To comparatively evaluate the performance of the recent competitive variants and newly proposed metaheuristics, 11 newly proposed metaheuristics and 4 variants of established metaheuristics are comprehensively compared on the CEC2017 benchmark suite. In addition, whether these algorithms have a search bias to the center of the search space is investigated. The results show that the performance of the newly proposed EBCM (effective butterfly optimizer with covariance matrix adaptation) algorithm performs comparably to the 4 well performing variants of the established metaheuristics and possesses similar properties and behaviors, such as convergence, diversity, exploration and exploitation trade-offs, in many aspects. The performance of all 15 of the algorithms is likely to deteriorate due to certain transformations, while the 4 state-of-the-art metaheuristics are less affected by transformations such as the shifting of the global optimal point away from the center of the search space. It should be noted that, except EBCM, the other 10 new algorithms proposed mostly during 2019-2020 are inferior to the well performing 2017 variants of differential evolution and evolution strategy in terms of convergence speed and global search ability on CEC 2017 functions.

Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as a generalized MMOP. In addition, the state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs. To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approaching the Pareto optimal front (PF). The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the $\epsilon$-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.

我们对两个单目标和两个多目标的全局全局优化算法进行了全面的全局灵敏度分析，作为算法配置问题。也就是说，我们研究了超参数对算法的直接效果和与其他超参数的效果的影响的影响质量。使用三种敏感性分析方法Morris LHS，Morris和Sobol，可以系统地分析协方差矩阵适应进化策略，差异进化，非主导的遗传算法III和多目标进化算法的可调型矩阵适应性进化策略，基于框架的分解，基于框架揭示，基于框架的遗传算法，超参数对抽样方法和性能指标的行为。也就是说，它回答了等问题，例如什么超参数会影响模式，它们的互动方式，相互作用的互动程度以及其直接影响程度。因此，超参数的排名表明它们的调整顺序，影响模式揭示了算法的稳定性。

现有的多策略自适应差分演进（de）通常涉及多种策略的试验，然后奖励更好的效率，具有更多资源。但是，剥削或探索战略的试验可能导致过度剥削或过度探索。为了提高性能，本文提出了一种新的策略适应方法，命名为显式适配方案（EA方案），其分离多种策略并将其按需采用它们。通过将演化过程划分为几个具有相似性选择（SCSS）代和自适应代的选择性候选者来完成的。在SCSS世代，通过利用平衡策略来学习利用和探索需求。为了满足这些需求，在自适应世代，另外两种策略，剥削或探索是自适应的。与其变体和其他适应方法相比，基准函数的实验研究证明了EA方案的有效性。此外，具有最先进的进化算法和基于群体智能的算法的性能比较表明EADE非常有竞争力。

聚类算法的全面基准是困难的两个关键因素：（i）〜这种无监督的学习方法的独特数学定义和（ii）〜某些聚类算法采用的生成模型或群集标准之间的依赖性的依赖性内部集群验证。因此，对严格基准测试的最佳做法没有达成共识，以及是否有可能在给定申请的背景之外。在这里，我们认为合成数据集必须继续在群集算法的评估中发挥重要作用，但这需要构建适当地涵盖影响聚类算法性能的各种属性集的基准。通过我们的框架，我们展示了重要的角色进化算法，以支持灵活的这种基准，允许简单的修改和扩展。我们说明了我们框架的两种可能用途：（i）〜基准数据的演变与一组手派生属性和（ii）〜生成梳理给定对算法之间的性能差异的数据集。我们的作品对设计集群基准的设计具有足够挑战广泛算法的集群基准，并进一步了解特定方法的优势和弱点。

信息科学的快速发展引起的“维度诅咒”在处理大数据集时可能会产生负面影响。在本文中，我们提出了Sparrow搜索算法（SSA）的一种变体，称为帐篷L \'evy飞行麻雀搜索算法（TFSSA），并使用它来选择包装模式中最佳的特征子集以进行分类。 SSA是最近提出的算法，尚未系统地应用于特征选择问题。通过CEC2020基准函数进行验证后，TFSSA用于选择最佳功能组合，以最大化分类精度并最大程度地减少所选功能的数量。将拟议的TFSSA与文献中的九种算法进行了比较。 9个评估指标用于正确评估和比较UCI存储库中21个数据集上这些算法的性能。此外，该方法应用于冠状病毒病（COVID-19）数据集，分别获得最佳的平均分类精度和特征选择的平均数量，为93.47％和2.1。实验结果证实了所提出的算法在提高分类准确性和减少与其他基于包装器的算法相比的选定特征数量方面的优势。

客户满意度在移动设备中的能源消耗至关重要。应用程序中最耗能的部分之一是图像。尽管具有不同质量的不同图像消耗了不同量的能量，但没有直接的方法来计算典型图像中操作的能量消耗。首先，本文调查了能源消耗与图像质量以及图像文件大小之间存在相关性。因此，这两者可以被视为能源消耗的代理。然后，我们提出了一种多目标策略，以增强图像质量并根据JPEG图像压缩中的定量表减少图像文件大小。为此，我们使用了两种一般的多目标元启发式方法：基于标量和基于帕累托。标量方法找到基于组合不同目标的单个最佳解决方案，而基于帕累托的技术旨在实现一组解决方案。在本文中，我们将策略纳入五种标量算法，包括能量感知的多目标遗传算法（ENMOGA），能量感知的多目标粒子群优化（ENMOPSO），能量感知的多目标多目标差异进化（ENMODE）（ENMODE）（ENMODE） ，能源感知的多目标进化策略（ENMOES）和能量感知的多目标模式搜索（ENMOPS）。此外，使用两种基于帕累托的方法，包括非主导的分类遗传算法（NSGA-II）和基于参考点的NSGA-II（NSGA-III），用于嵌入方案，以及两种基于帕累托的算法，即两种基于帕累托的算法，即提出了Ennsgaii和Ennsgaiii。实验研究表明，基线算法的性能通过将拟议策略嵌入到元启发式算法中来提高。

基准和性能分析在理解迭代优化启发式（IOHS）的行为中发挥着重要作用，例如本地搜索算法，遗传和进化算法，贝叶斯优化算法等。然而，这项任务涉及手动设置，执行和分析实验单独的基础，这是艰苦的，可以通过通用和设计精心设计的平台来缓解。为此，我们提出了Iohanalyzer，一种用于分析，比较和可视化IOH的性能数据的新用户友好的工具。在R和C ++中实现，Iohanalyzer是完全开源的。它可以在Cran和GitHub上获得。 Iohanalyzer提供有关固定目标运行时间的详细统计信息以及具有实际值的Codomain，单目标优化任务的基准算法的固定预算性能。例如，在多个基准问题上的性能聚合是可能的，例如以经验累积分布函数的形式。 Iohanalyzer在其他性能分析包上的主要优点是其高度交互式设计，允许用户指定对其实验最有用的性能测量，范围和粒度，以及不仅分析性能迹线，还可以分析演变动态状态参数。 Iohanalyzer可以直接从主基准平台处理性能数据，包括Coco平台，JOVERRAD，SOS平台和iohExperenter。提供R编程接口，供用户更倾向于对实现的功能进行更精细的控制。

In the field of derivative-free optimization, both of its main branches, the deterministic and nature-inspired techniques, experienced in recent years substantial advancement. In this paper, we provide an extensive computational comparison of selected methods from each of these branches. The chosen representatives were either standard and well-utilized methods, or the best-performing methods from recent numerical comparisons. The computational comparison was performed on five different benchmark sets and the results were analyzed in terms of performance, time complexity, and convergence properties of the selected methods. The results showed that, when dealing with situations where the objective function evaluations are relatively cheap, the nature-inspired methods have a significantly better performance than their deterministic counterparts. However, in situations when the function evaluations are costly or otherwise prohibited, the deterministic methods might provide more consistent and overall better results.

本文提出了一种名为Duck Sharm算法（DSA）的群体智能的优化算法。该算法通过寻找鸭子群的食物来源和觅食行为的启发。通过使用十八个基准函数来验证DSA的性能，其中统计（最佳，平均值，标准偏差和平均运行时间）结果与粒子群优化（PSO），Firefly算法（FA ），鸡肉群优化（CSO），灰狼优化器（GWO），正弦余弦算法（SCA）和海洋捕食者算法（MPA）和ArchImedes优化算法（AOA）。此外，使用比较结果的Wilcoxon Rank-Sum测试，Friedman测试和收敛曲线来证明DSA对其他算法的优越性。结果表明，DSA是在收敛速度和勘探开发平衡方面是求解高维优化功能的高性能优化方法。此外，DSA应用于两个约束工程问题的最佳设计（三条桁架问题，以及锯木厂运行问题）。此外，还用于分析所提出的DSA的性能的四个工程约束问题。总体而言，比较结果表明，DSA是一种有前途和非常竞争力的算法，用于解决不同的优化问题。

分类是数据挖掘和机器学习领域中研究最多的任务之一，并且已经提出了文献中的许多作品来解决分类问题，以解决多个知识领域，例如医学，生物学，安全性和遥感。由于没有单个分类器可以为各种应用程序取得最佳结果，因此，一个很好的选择是采用分类器融合策略。分类器融合方法成功的关键点是属于合奏的分类器之间多样性和准确性的结合。借助文献中可用的大量分类模型，一个挑战是选择最终分类系统的最合适的分类器，从而产生了分类器选择策略的需求。我们通过基于一个称为CIF-E（分类器，初始化，健身函数和进化算法）的四步协议的分类器选择和融合的框架来解决这一点。我们按照提出的CIF-E协议实施和评估24种各种集合方法，并能够找到最准确的方法。在文献中最佳方法和许多其他基线中，还进行了比较分析。该实验表明，基于单变量分布算法（UMDA）的拟议进化方法可以超越许多著名的UCI数据集中最新的文献方法。