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。

The JPEG standard is widely used in different image processing applications. One of the main components of the JPEG standard is the quantisation table (QT) since it plays a vital role in the image properties such as image quality and file size. In recent years, several efforts based on population-based metaheuristic (PBMH) algorithms have been performed to find the proper QT(s) for a specific image, although they do not take into consideration the user's opinion. Take an android developer as an example, who prefers a small-size image, while the optimisation process results in a high-quality image, leading to a huge file size. Another pitfall of the current works is a lack of comprehensive coverage, meaning that the QT(s) can not provide all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user's opinion in the compression process, the file size of the output image can be controlled by a user in advance. Second, to tackle the lack of comprehensive coverage, we suggest a novel representation. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both changes in representation and objective function are independent of the search strategies and can be used with any type of population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.

语义已成为遗传编程（GP）研究的关键话题。语义是指在数据集上运行时GP个体的输出（行为）。专注于单目标GP中语义多样性的大多数作品表明它在进化搜索方面是非常有益的。令人惊讶的是，在多目标GP（MOGP）中，在语义中进行了小型研究。在这项工作中，我们跨越我们对Mogp中语义的理解，提出SDO：基于语义的距离作为额外标准。这自然鼓励Mogp中的语义多样性。为此，我们在第一个帕累托前面的较密集的区域（最有前途的前沿）找到一个枢轴。然后，这用于计算枢轴与人群中的每个人之间的距离。然后将所得到的距离用作优化以优化以偏及语义分集的额外标准。我们还使用其他基于语义的方法作为基准，称为基于语义相似性的交叉和语义的拥挤距离。此外，我们也使用NSGA-II和SPEA2进行比较。我们使用高度不平衡二进制分类问题，一致地展示我们所提出的SDO方法如何产生更多非主导的解决方案和更好的多样性，导致更好的统计学显着的结果，与其他四种方法相比，使用超卓越症结果作为评估措施。

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。

Explicitly accounting for uncertainties is paramount to the safety of engineering structures. Optimization which is often carried out at the early stage of the structural design offers an ideal framework for this task. When the uncertainties are mainly affecting the objective function, robust design optimization is traditionally considered. This work further assumes the existence of multiple and competing objective functions that need to be dealt with simultaneously. The optimization problem is formulated by considering quantiles of the objective functions which allows for the combination of both optimality and robustness in a single metric. By introducing the concept of common random numbers, the resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II). The computational cost of such an approach is however a serious hurdle to its application in real-world problems. We therefore propose a surrogate-assisted approach using Kriging as an inexpensive approximation of the associated computational model. The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate the quantiles. Finally, the methodology is adapted to account for mixed categorical-continuous parameters as the applications involve the selection of qualitative design parameters as well. The methodology is first applied to two analytical examples showing its efficiency. The third application relates to the selection of optimal renovation scenarios of a building considering both its life cycle cost and environmental impact. It shows that when it comes to renovation, the heating system replacement should be the priority.

传感器节点（SNS）的部署总是在无线传感器网络（WSN）的系统性能中起决定性作用。在这项工作中，我们提出了一种实用异构WSN的最佳部署方法，该方法可以深入了解可靠性和部署成本之间的权衡。具体而言，这项工作旨在提供SNS的最佳部署，以最大程度地提高覆盖率和连接学位，同时最大程度地减少整体部署成本。此外，这项工作充分考虑了SNS的异质性（即差异化的传感范围和部署成本）和三维（3-D）部署方案。这是一个多目标优化问题，非凸，多模态和NP-HARD。为了解决它，我们开发了一种新型的基于群体的多目标优化算法，称为竞争性多目标海洋掠食者算法（CMOMPA），其性能通过与十种其他多个多目标优化的全面比较实验验证算法。计算结果表明，在收敛性和准确性方面，CMOMPA优于他人，并且在多模式多目标优化问题上表现出卓越的性能。还进行了足够的模拟来评估基于CMOMPA的最佳SNS部署方法的有效性。结果表明，优化的部署可以平衡部署成本，感知可靠性和网络可靠性之间的权衡平衡。源代码可在https://github.com/inet-wzu/cmompa上找到。

Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a better Pareto optimal solution, metaheuristic optimization methods are widely used in many studies. However, the main drawback is the exploration of a large search space. Another problem with multi-objective feature selection approaches is the interaction between features. Selecting correlated features has negative effect on classification performance. To tackle these problems, we present a novel multi-objective feature selection method that has several advantages. Firstly, it considers the interaction between features using an advanced probability scheme. Secondly, it is based on the Pareto Archived Evolution Strategy (PAES) method that has several advantages such as simplicity and its speed in exploring the solution space. However, we improve the structure of PAES in such a way that generates the offsprings, intelligently. Thus, the proposed method utilizes the introduced probability scheme to produce more promising offsprings. Finally, it is equipped with a novel strategy that guides it to find the optimum number of features through the process of evolution. The experimental results show a significant improvement in finding the optimal Pareto front compared to state-of-the-art methods on different real-world datasets.

HyperParameter Optimization（HPO）是一种确保机器学习（ML）算法最佳性能的必要步骤。已经开发了几种方法来执行HPO;其中大部分都集中在优化一个性能措施（通常是基于错误的措施），并且在这种单一目标HPO问题上的文献是巨大的。然而，最近似乎似乎侧重于同时优化多个冲突目标的算法。本文提出了对2014年至2020年的文献的系统调查，在多目标HPO算法上发布，区分了基于成逐的算法，Metamodel的算法以及使用两者混合的方法。我们还讨论了用于比较多目标HPO程序和今后的研究方向的质量指标。

可以将多任务学习（MTL）范例追溯到Caruana（1997）的早期纸张中，其中表示可以使用来自多个任务的数据，其目的是在独立地学习每个任务的旨在获得更好的性能。 MTL与相互矛盾的目标的解决方案需要在它们中进行折衷，这通常超出了直线组合可以实现的。理论上原则和计算有效的策略正在寻找不受他人主导的解决方案，因为它在帕累托分析中解决了它。多任务学习环境中产生的多目标优化问题具有特定的功能，需要adhoc方法。对这些特征的分析和新的计算方法的提议代表了这项工作的重点。多目标进化算法（MOEAS）可以容易地包括优势的概念，因此可以分析。 MOEAS的主要缺点是关于功能评估的低样本效率。此缺点的关键原因是大多数进化方法不使用模型来近似于目标函数。贝叶斯优化采用基于代理模型的完全不同的方法，例如高斯过程。在本文中，输入空间中的解决方案表示为封装功能评估中包含的知识的概率分布。在这种概率分布的空间中，赋予由Wassersein距离给出的度量，可以设计一种新的算法MOEA / WST，其中模型不直接在目标函数上，而是在输入空间中的对象的中间信息空间中被映射成直方图。计算结果表明，MoEA / WST提供的样品效率和帕累托集的质量明显优于标准MoEa。

在多目标优化中，一组具有各种功能的可扩展测试问题使研究人员可以调查和评估不同优化算法的能力，因此可以帮助他们设计和开发更有效，更有效的方法。现有的测试问题套件主要集中在所有目标彼此完全冲突的情况下。在这种情况下，目标空间中的M-Obigntive优化问题具有（M-1）维帕累托前沿。但是，在某些优化问题中，目标之间可能存在意外的特征，例如冗余。某些目标的冗余可能会导致具有堕落的帕累托正面的多物镜问题，即，$ m $ - 目标问题的帕累托正面的尺寸小于（M-1）。在本文中，我们系统地研究了退化的多目标问题。我们抽象了退化问题的三个一般特征，这些特征未在文献中进行制定和系统地研究。基于这些特征，我们提出了一组测试问题，以支持在具有冗余目标的情况下对多目标优化算法进行研究。据我们所知，这项工作是第一项明确提出退化问题的三个特征，从而使所得的测试问题的一般性具有一般性的特征，与为特定目的设计的现有测试问题相比（例如，可视化），则允许所得的测试问题。 ）。

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.

在进化多目标优化领域，决策者（DM）涉及相互冲突的目标。在现实世界中，通常存在多个DM，每个DM都涉及这些目标的一部分。提出了多方多目标优化问题（MPMOPS）来描绘拖把，其中涉及多个决策者，每个方都关注所有目标的某些目标。但是，在进化计算字段中，对mpmops的关注不多。本文基于距离最小化问题（DMP）构建了一系列MPMOP，它们的Pareto最佳解决方案可以生动地可视化。为了解决MPMOPS，新提出的算法OPTMPNDS3使用多方初始化方法来初始化总体，并带Jade2操作员生成后代。在问题套件上，将OPTMPNDS3与Optall，OptMPND和OptMPNDS2进行了比较。结果表明OPTMPNDS3与其他算法具有很强的可比性

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.

自由形式变形模型可以通过在图像上操纵控制点晶格来代表广泛的非刚性变形。但是，由于大量参数，由于适应性景观的复杂性，将自由形式变形模型直接拟合到变形图像以进行变形估计是一项挑战。在本文中，我们根据每个控制点影响的区域相互重叠的事实，将注册任务作为多目标优化问题（MOP）。具体而言，通过将模板图像划分为几个区域并独立测量每个区域的相似性，可以通过使用现成的多目标进化算法（MOEAS）来解决多个目标，并可以通过解决拖把来实现变形估计。此外，图像金字塔与控制点网格细分结合使用了粗到五个策略。具体而言，当前图像级别的优化候选解决方案是由下一个级别继承的，这增加了处理大变形的能力。此外，提出了一个后处理过程，以利用帕累托最佳解决方案生成单个输出。对合成图像和现实世界图像的比较实验显示了我们变形估计方法的有效性和实用性。

许多现实世界优化问题，如工程最优设计，最终可以被建模为必须解决的相应多目标优化问题（MOPS），以获得近似帕累托最佳前端。基于分解（MOEA / D）的多目标进化算法被认为是解决MOP的明显有希望的方法。最近的研究表明，具有均匀重量载体的MoEA / D非常适合于普通帕累托最佳前端的拖把，但在多样性方面的性能通常会在解决带有不规则帕累托最佳方向时造成拖镜时劣化。以这种方式，通过该算法获得的解决方案集不能为决策者提供更合理的选择。为了有效地克服这一缺点，我们通过众所周知的Pascoletti-Serafini标定方法和多参考点的新策略提出了一种改进的MoA / D算法。具体地，该策略包括由等距分区和投影的技术产生的参考点的设置和调整组成。对于性能评估，将所提出的算法与现有的四个最先进的多目标进化算法进行比较，这些算法与各种类型的帕累托最优前锋和两个现实世界拖把的基准测试问题相比，包括舱口盖设计和火箭喷射器设计在工程优化中。根据实验结果，所提出的算法表现出比其他比较算法更好的分集性能。

活跃成像的敏捷地球观测卫星（AI-Aea）是新一代敏捷的地球观测卫星（AEOS）。随着观察和主动Im-gering的更新能力，AI-Aeos的观察能力提高了AEOS的观察能力，并提供了观察地面目标的其他方法。然而，这使得这些敏捷地球观察卫星的观察计划问题更加复杂，尤其是在考虑多条纹地面目标时。在本文中，我们研究了主动图像敏捷地球观察卫星（MOSP）的多strip观察计划问题。向MOSP提出了双向目标优化模型，以及一种自适应的模因算法，该算法整合了自适应大型邻里搜索算法（ALNS）和非主导分类遗传算法II（NSGA-II）的组合功率。提出了广泛的计算实验的结果，这些结果揭示了ALNS和NSGA-II在一致的工作中产生了出色的结果。我们的模型比现有模型更通用，并在应用问题解决方面提供了增强的功能。

在过去的几十年中，经典的车辆路由问题（VRP），即为车辆分配一组订单并规划他们的路线已经被密集研究。仅作为车辆的订单分配和他们的路线已经是一个NP完整的问题，因此在实践中的应用通常无法考虑在现实世界应用中应用的约束和限制，所谓的富VRP所谓的富VRP（RVRP）并且仅限于单一方面。在这项工作中，我们融入了主要的相关真实限制和要求。我们提出了一种两级策略和时间线窗口和暂停时间的时间线算法，并将遗传算法（GA）和蚁群优化（ACO）单独应用于问题以找到最佳解决方案。我们对四种不同问题实例的评估，针对四个最先进的算法表明，我们的方法在合理的时间内处理所有给定的约束。

大多数现实世界中的问题本质上都是多模式，由多个最佳值组成。多模式优化定义为找到函数的多个全局和局部优化（与单个解决方案相反）的过程。它使用户可以根据需要在不同的解决方案之间切换，同时仍保持最佳系统性能。基于经典梯度的方法未能用于优化问题，因为目标函数是不连续的或不可差的。与需要多个重新启动的经典优化技术相比，进化算法（EAS）能够在单个算法运行中以单个算法运行中的多个解决方案找到多个解决方案，以找到不同的解决方案。因此，已经提出了一些EA来解决此类问题。但是，差异进化（DE）算法是一种基于人群的启发式方法，可以解决此类优化问题，并且可以易于实施。多模式优化问题（MMOP）的潜在挑战是有效地搜索功能空间以准确地定位大多数峰。优化问题可能是最大程度地减少或最大化给定的目标函数，我们旨在解决本研究中多模式功能的最大化问题。因此，我们提出了一种称为增强对立差异进化（EODE）算法的算法来求解MMOP。拟议的算法已在IEEE进化计算（CEC）2013基准功能上进行了测试，并且与现有的最新方法相比，它取得了竞争性结果。

In today's uncertain and competitive market, where enterprises are subjected to increasingly shortened product life-cycles and frequent volume changes, reconfigurable manufacturing systems (RMS) applications play a significant role in the manufacturing industry's success. Despite the advantages offered by RMS, achieving a high-efficiency degree constitutes a challenging task for stakeholders and decision-makers when they face the trade-off decisions inherent in these complex systems. This study addresses work tasks and resource allocations to workstations together with buffer capacity allocation in RMS. The aim is to simultaneously maximize throughput and minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach support decision-makers with discovered knowledge to further understand the RMS design. In particular, this study presents a problem-specific customized SMO combined with a novel flexible pattern mining method for optimizing RMS and conducting post-optimal analyzes. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision-support and production planning of RMS.