Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

云母钛珠光颜料的研究进展

珠光颜料是一种能显示珍珠光色泽的颜料,在生活中可谓无所不在。以云母钛珠光颜料为研究对象,综述了近年来国内外关于云母钛珠光颜料的制备及应用方面的研究进展。通过对珠光颜料的分类和云母钛珠光颜料的制备方法进行归纳,比较了各制备方法的特点,分析了不同制备方法的选择对于珠光颜料应用效果的影响以及工业化的可行性。此外,对当前技术路线和未来研究方向提出了新的建议和展望。

机器学习在无线网络优化中的应用研究

目前网络优化问题仍然基本依靠手工处理,存在着效率低下、效果难以有效保证的问题.在工作量快速增长的背景下,迫切需要提高自动化和智能化水平.本文对利用机器学习提高优化工作效率和质量进行了研究,提出了机器学习在自动问题检测、自动根因分析和智能优化方案制定等方面的应用设想,给出了监督学习和非监督学习的应用方法,并提供了两个具体应用案例.

筒仓滑模施工砼质量控制及常见问题的处理

在水泥厂筒仓滑模施工过程中,根据工程资源配置和自然条件等因素,确定砼坍落度、初凝时间等,提前作好砼试配工作,确保滑模施工顺利完成。滑模施工过程中应预防和及时处理滑模平台扭转和倾斜、筒仓垂直度偏差等常见问题。

推进审判体系和审判能力现代化视阈下民事速裁机制构建的逻辑与路径

速裁所指的不是一种程序,而是一种机制,故完善速裁不能仅将眼光局限于"程序视角",而应当着眼于"机制视角"。当前,我国民事速裁尚未形成完备的机制,表现在:大量非速裁案件纳入速裁范围,导致"越位";速裁案件审理规则不完备,导致"错位";未针对速裁案件设置对应的审判组织机构和相应的审级运转衔接模式,导致"缺位"。完善民事速裁机制,应设置专门的速裁法院(庭),并在中级法院设置速裁庭,实现"补位";以标的额大小作为繁简分流的首要标准,实现"归位";根据速裁案件的特点设置相应的审理规则,实现"正位"。

海上风电基础桩多丝埋弧焊推广研究与探讨

多丝埋弧焊接技术是风电管桩生产中高效的焊接工艺方法,欧洲风电管桩制造已较为广泛地应用多丝埋弧焊焊接。根据多丝焊在国内海上管桩应用中所出现的几个瓶颈问题,基于坡口的制定与加工、焊接工艺数据库设置、设备防窜联控、设备焊接跟踪等几个方面的研究,提出了目前多丝埋弧焊接问题的解决办法。经过多年对国外风电制作方法持续跟踪研究,总结出我国风电制造与国外先进制造的差距,剖析了最新最先进的风电生产技术,以及多丝焊接能成功应用的原因,为国内风电发展提供了技术研究方向。研究结论可供相关风电管桩生产单位和焊机设备制造单位参考。

Mutual Restriction between Concurrence and Intrinsic Concurrence for Arbitrary Two-Qubit States

Concurrence is viewed as the most commonly approach for quantifying entanglement of two-qubit states,while intrinsic concurrence contains concurrence of four pure states consisting of a special pure state ensemble concerning an arbitrary two-qubit state.Thus,a natural question arises:Whether there is a specified relation between them.We firstly examine the relation between concurrence and intrinsic concurrence for the maximally nonlocal mixed state under a special unitary operation,which is not yet rigorously proved.In order to obtain a general result,we investigate the relation between concurrence and intrinsic concurrence using randomly generated two-qubit states,and derive an inequality relation between them.Finally,we take into account the relation between concurrence and intrinsic concurrence in open systems,and reveal the ratio of the two quantum resources,which is only correlated with the experiencing channels.

Clinical analysis of 45 cases of surgical treatment for inner thigh primary soft tissue sarcoma

Objective: We aimed to study the relationship between clinical effect and surgical methods of inner thigh primary soft tissue sarcomas. Methods: Wide or radical resection were performed in 45 cases of soft tissue sarcomas, including 20 cases of postoperative recurrence after radiation therapy, 7 cases of first treatment. Thirty-six cases received 4–6 cycles of postoperative chemotherapy. Results: Thirty-eight of 45 cases were followed up for 1–5 years, with 5 case of recurrence and 6 cases of distant metastasis. Conclusion: The inner thigh primary soft tissue sarcoma can be effectively treated with wide or radical resection.

Exploring a Promising Region and Enhancing Decision Space Diversity for Multimodal Multi-Objective Optimization

During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization community.Recently,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space convergence.However,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space diversity.To overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling MMOPs.Unlike traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving MMOPs.Furthermore,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding distance.Based on the above methods,we propose a novel dual-population-based coevolutionary algorithm.Experimental studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different MMOPs.The effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.

Inequality relations for the hierarchy of quantum correlations in two-qubit systems

Entanglement,quantum steering and Bell nonlocality can be used to describe the distinct quantum correlations of quantum systems.Because of their different characteristics and application fields,how to divide them quantitatively and accurately becomes particularly important.Based on the sufficient and necessary criterion for quantum steering of an arbitrary two-qubit T-state,we derive the inequality relations between quantum steering and entanglement as well as between quantum steering and Bell nonlocality for the T-state.Additionally,we have verified those relations experimentally.

Dynamical characteristic of measurement uncertainty under Heisenberg spin models with Dzyaloshinskii-Moriya interactions

The dynamics of measurement's uncertainty via entropy for a one-dimensional Heisenberg XYZ mode is examined in the presence of an inhomogeneous magnetic field and Dzyaloshinskii-Moriya (DM) interaction. It shows that the uncertainty of interest is intensively in connection with the filed's temperature, the direction-oriented coupling strengths and the magnetic field. It turns out that the stronger coupling strengths and the smaller magnetic field would induce the smaller measurement's uncertainty of interest within the current spin model. Interestingly, we reveal that the evolution of the uncertainty exhibits quite different dynamical behaviors in antiferromagnetic (Ji> 0) and ferromagnetic (Ji< 0) frames. Besides, an analytical solution related to the systematic entanglement (i.e., concurrence) is also derived in such a scenario. Furthermore, it is found that the DM-interaction is desirably working to diminish the magnitude of the measurement's uncertainty in the region of high-temperature. Finally, we remarkably offer a result ful strategy to govern the ent ropy-based uncertainty through utilizing quantum weak measurements, being of fundamentally importance to quantum measurement estimation in the context of solid-state-based quantum information processing and comp ut at ion.

Complementary relation between quantum entanglement and entropic uncertainty

Quantum entanglement is regarded as one of the core concepts,which is used to describe the nonclassical correlation between subsystems,and entropic uncertainty relation plays a vital role in quantum precision measurement.It is well known that entanglement of formation can be expressed by von Neumann entropy of subsystems for arbitrary pure states.An interesting question is naturally raised:is there any intrinsic correlation between the entropic uncertainty relation and quantum entanglement?Or if the relation can be applied to estimate the entanglement.In this work,we focus on exploring the complementary relation between quantum entanglement and the entropic uncertainty relation.The results show that there exists an inequality relation between both of them for an arbitrary two-qubit system,and specifically the larger uncertainty will induce the weaker entanglement of the probed system,and vice versa.Besides,we use randomly generated states as illustrations to verify our results.Therefore,we claim that our observations might offer and support the validity of using the entropy uncertainty relation to estimate quantum entanglement.

The uncertainty and quantum correlation of measurement in double quantum-dot systems

In this work,we study the entropic uncertainty and quantum discord in two double-quantum-dot(DQD)system coupled via a transmission line resonator(TLR).Explicitly,the dynamics of the systemic quantum correlation and measured uncertainty are analysed with respect to a general Xtype state as the initial state.Interestingly,it is found that the different parameters,including the eigenvalueαof the coherent state,detuning amountδ,frequencyωand the coupling constant g,have subtle effects on the dynamics of the entropic uncertainty,such as the oscillation period of the uncertainty.It is clear to reveal that the quantum discord and the lower bound of the entropic uncertainty are anti-correlated when the initial state of the system is the Werner-type state,while quantum discord and the lower bound of the entropic uncertainty are not anti-correlated when the initial state of the system is the Bell-diagonal state.Thereby,we claim that the current investigation would provide an insight into the entropic uncertainty and quantum correlation in DQDs system,and are basically of importance to quantum precision measurement in practical quantum information processing.