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In this study, we consider the scenario that differential evolution ( DE) is applied for global numerical optimization and the index-based neighborhood information of population is used for enhancing the performance of DE. Althoug...
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In this study, we consider the scenario that differential evolution ( DE) is applied for global numerical optimization and the index-based neighborhood information of population is used for enhancing the performance of DE. Although many methods are developed under this scenario, neighborhood information of current population has not been systematically exploited in the DE algorithm design. Furthermore, previous studies have shown the effect of neighborhood topology interacted with the function being solved. However, there are few investigations of DE that consider different topologies for different functions during the evolutionary process. Motivated by these observations, a new DE framework, named neighborhood-adaptive DE (NaDE), is presented. In NaDE, a pool of index-based neighborhood topologiesis firstly used to define multiple neighborhood relationships for each individual and then the neighborhood relationships are adaptively selected for the specific functions during the evolutionary process. In this way, a more appropriate neighborhood relationship for each individual can be determined adaptively to match different phases of the search process for the function being solved. After that, a neighborhood-dependent directional mutation operator is introduced into NaDE to generate a new solution with the selected neighborhood topology. Being a general framework, NaDE is easy to implement and can be realized with most existing DE algorithms. In order to test the effectiveness of the proposed framework, we have evaluated NaDE via investigating several instantiations of it. Experimental results have shown that NaDE generally outperforms its corresponding DE algorithm on different kinds of optimization problems. Moreover, the synergy among different neighborhood topologies in NaDE is also revealed when compared with the DE variants with single neighborhood topology. (C) 2017 Elsevier B.V. All rights reserved.
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Differential evolution (DE) relies mainly on its mutation mechanism to guide its search. Generally, the parents involved in mutation are randomly selected from the current population. Although such a mutation strategy is easy to u...
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Differential evolution (DE) relies mainly on its mutation mechanism to guide its search. Generally, the parents involved in mutation are randomly selected from the current population. Although such a mutation strategy is easy to use, it is inefficient for solving complex problems. Hence, how to utilize population information to further enhance the search ability of the mutation operator has become one of the most salient and active topics in DE. To address this issue, a new DE framework with the concept of index-based neighborhood, is proposed in this study. The proposed framework is named as neighborhood guided DE (NGDE). In NGDE, a neighborhood guided selection (NGS) is introduced to guide the mutation process by extracting the promising search directions with the neighborhood information. NGS includes four main operators: neighborhood construction, neighbors grouping, two-level neighbors ranking, and parents selection. With these four operators, NGS can utilize the topology and fitness information of population simultaneously. To evaluate the effectiveness of the proposed approach, NGS is applied to several original and advanced DE algorithms. Experimental results have shown that NGDE generally outperforms most of the corresponding DE algorithms on different kinds of optimization problems.
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Differential evolution (DE) is an efficient and robust evolutionary algorithm (EA), that has been widely and successfully applied to solve global optimization problems in diverse real world applications. As the population structur...
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Differential evolution (DE) is an efficient and robust evolutionary algorithm (EA), that has been widely and successfully applied to solve global optimization problems in diverse real world applications. As the population structure has a major influence on the behavior of an EA, effectively incorporating population topology into DE has recently attracted increasing attention. Previous works have shown the effectiveness of different topologies in improving the performance of DE and revealed that different topologies can have different effects on the population's ability to solve optimization problems. However, the synergy of different topologies for the problems being solved has not been systematically investigated in most DE variants. Moreover, individuals with different fitness values play different roles in guiding the search during the evolutionary process. Nevertheless, the individual dependent roles are not considered in most DE variants that consider the population topology. To overcome these drawbacks and utilize the information that is derived from the differences between the fitness values of individuals for topology adaption, we propose a multi-topology-based DE (MTDE) algorithm that includes an ensemble of multiple population topologies (MPT), an individual-dependent adaptive topology selection (ITS) scheme, and a topology-dependent mutation (TDM) strategy. In the ensemble of MPT, multiple population topologies with different degrees of connectivity are employed. In the ITS scheme, each individual adaptively selects the topology that is most compatible its role in guiding the search based on its fitness value. In the TDM strategy, the parents for mutation are chosen from the neighborhood of the current individual based on the corresponding topology to generate offspring. The effectiveness of the proposed algorithm is extensively evaluated on a suite of benchmark functions. Experimental results demonstrate the competitive performance of MTDE when compared
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Li-chalcogen batteries, especially the Li-S batteries (LSBs), have received paramount interests as next generation energy storage techniques because of their high theoretical energy densities. However, the associated challenges ne...
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Li-chalcogen batteries, especially the Li-S batteries (LSBs), have received paramount interests as next generation energy storage techniques because of their high theoretical energy densities. However, the associated challenges need to be overcome prior to their commercialization. Elemental selenium, another chalcogen member, would be an attractive alternative to sulfur owing to its higher electronic conductivity, comparable capacity density, and moreover, excellent compatibility with carbonate electrolytes. Unlike LSBs, the research and development of Li-Se batteries (LSeBs) have garnered burgeoning attention but are still in their infant stage, where a comprehensive yet in-depth overview is highly imperative to guide future research. Herein, a critical review of LSeBs, in terms of the underlying mechanisms, cathode design, blocking layer engineering, and emerging solid-state electrolytes is provided. First, the electrolyte-dependent electrochemistry of LSeBs is discussed. Second, the advances in Se-based cathodes are comprehensively summarized, especially highlighting the state-of-the-art SexSy cathodes, and mainly focusing on their structures, compositions, and synthetic strategies. Third, the versatile separators/interlayers optimization and interface regulation are outlined, with a particular focus on the emerging solid-state electrolytes for advanced LSeBs. Last, the remaining challenges and research orientations in this booming field are proposed, which are expected to motivate more insightful works.
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Over the past few decades, the intelligent transportation system (ITS) have emerged with new technologies and becomes the data-driven ITS, because the substantial amount of data is assembled from the multiple sources. Vehicular Ad...
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Over the past few decades, the intelligent transportation system (ITS) have emerged with new technologies and becomes the data-driven ITS, because the substantial amount of data is assembled from the multiple sources. Vehicular Ad hoc networks (VANETs), are a particular case of ad hoc networks that are used in the smart ITS. VANETs have become one of the most, encouraging, promising, and fastest-growing subsets of the mobile ad hoc networks (MANETs). They are comprised of smart vehicles and roadside units (RSUs) and on-board units (OBUs) which communicate through unreliable wireless media. Other than lacking infrastructure, delivering entities move with different increasing speeds. Thus, this delays establishing reliable end-to-end communication paths and having efficient data transfer. In this manner, VANETs have diverse system concerns and security difficulties in getting the accessibility of ubiquitous availability, secure communication, interchanges, and reputation management system. Which influence the trust in collaboration and arrangement between the portable system. By their fluctuation in nature, they are genuinely defenseless against assaults, which may result in life-jeopardizing circumstances. In this survey, we provide an extensive overview of the ITS and the evolution of ITS to VANETs. We provide the details of VANETs, discussed the privacy and security attacks in VANETs with their applications and challenges. We address the effectiveness of VANETs and cloud computing with architecture and related privacy and security issues. We also examined the communication protocols for each network layer with the relevant attacks occurred at each layer. We also discussed the potential benefits of the different proposed techniques related to VANETs, application, and challenges in details. In the end, we provide a conclusion with some open and emerging issues in VANETs. (C) 2019 Elsevier Inc. All rights reserved.
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This study investigates the robust resource-constrained max-NPV project problem with stochastic activity duration. First, the project net present value (NPV) and the expected penalty cost are proposed to measure quality robustness...
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This study investigates the robust resource-constrained max-NPV project problem with stochastic activity duration. First, the project net present value (NPV) and the expected penalty cost are proposed to measure quality robustness and solution robustness from the perspective of discounted cash flows, respectively. Then, a composite robust scheduling model is proposed in the presence of activity duration variability and a two-stage algorithm that integrates simulated annealing and tabu search is developed to deal with the problem. Finally, an extensive computational experiment demonstrates the superiority of the combination between quality robustness and solution robustness as well as the effectiveness of the proposed two-stage algorithm for generating project schedules compared with three other algorithms, namely simulated annealing, tabu search, and multi-start iterative improvement method. Computational results indicate that the proactive project schedules with composite robustness not only can effectively protect the payment plan from disruptions through allocating appropriate time buffers, but also can achieve a remarkable performance with respect to the project NPV.
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Due to the development of microprocessor technology, there are more than 20 billion sensor-based devices connected to the Internet of Things (IoT) to monitor physical phe-nomena and events. To reduce the energy used by idle listen...
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Due to the development of microprocessor technology, there are more than 20 billion sensor-based devices connected to the Internet of Things (IoT) to monitor physical phe-nomena and events. To reduce the energy used by idle listening, a low-duty cycle is often used in an event-sparse wireless sensor network. However, low-duty cycles bring large end-to-end delays. In this paper, a listen interval adaptive adjustment (LIAA) scheme is proposed to adjust the listen interval (LI) of a node to reduce end-to-end delays while maintaining the long lifetime of a network. The key idea is to make full use of the energy consumption imbalance in a network, to allow nodes away from the sink to use the resid-ual energy to add listen intervals. The LIAA scheme has 3 sub-strategies. One is the basic add listen interval (BALI) strategy, in which the parent node adds listen intervals in the fixed active slots of its child nodes. Another strategy is the consecutive listen (CL) scheme, which is based on BALI, and listen intervals are added consecutively. The third strategy is the random add listen interval (RALI) scheme, which uses the idea of randomness to add listen intervals. The extensive theoretical analysis and experimental results show that the LIAA scheme proposed in this paper has better performance. Compared with the tradi-tional scheme, the delays in the BALI scheme, CL scheme and RALI scheme were reduced by 24.03%, 23.45% and 39.41%, respectively, while the lifetime of the network was maintained. (c) 2021 Elsevier Inc. All rights reserved.
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Differential evolution (DE) is a powerful evolutionary algorithm (EA) for numerical optimization. It has been successfully used in various scientific and engineering fields. In most of the DE algorithms, the neighborhood and direc...
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Differential evolution (DE) is a powerful evolutionary algorithm (EA) for numerical optimization. It has been successfully used in various scientific and engineering fields. In most of the DE algorithms, the neighborhood and direction information are not fully and simultaneously exploited to guide the search. Most recently, to make full use of these information, a DE framework with neighborhood and direction information (NDi-DE) was proposed. It was experimentally demonstrated that NDi-DE was effective for most of the DE algorithms. However, the performance of NDi-DE heavily depends on the selection of direction information. To alleviate this drawback and improve the performance of NDi-DE, the adaptive operator selection (AOS) mechanism is introduced into NDi-DE to adaptively select the direction information for the specific DE mutation strategy. Therefore, a new DE framework, adaptive direction information based NDi-DE (aNDi-DE), is proposed in this study. With AOS, the good balance between exploration and exploitation of aNDi-DE can be dynamically achieved. In order to evaluate the effectiveness of aNDi-DE, the proposed framework is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results show that aNDi-DE is able to adaptively select the most suitable type of direction information for the specific DE mutation strategy during the evolutionary process. The efficiency and robustness of aNDi-DE are also confirmed by comparing with NDi-DE.
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Ubiquitination regulates almost every life process of eukaryotes. The study of the ubiquitin (Ub) coupling or decoupling process and the interaction study of Ub-Ub binding protein have always been the central focus. However, such ...
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Ubiquitination regulates almost every life process of eukaryotes. The study of the ubiquitin (Ub) coupling or decoupling process and the interaction study of Ub-Ub binding protein have always been the central focus. However, such studies are challenging, owing to the transient nature of Ub-coupling enzymes and deubiquitinases in the reactions, as well as the difficulty in preparing large quantities of polyubiquitinated samples. Here we describe a recently developed strategy for the efficient preparation of analogs of Ub chains and analogs for Ub coupling and uncoupling intermediates, which facilitate the study of the ubiquitination process. The strategy includes mainly the following steps: (i) the bifunctional molecule conjugation on the only cysteine (Cys) residue of a target protein (usually a Ub or Ub-conjugating enzyme), exposing an orthogonal reactive site for native chemical ligation; (ii) covalent ligation with a Ub-derived thioester, exposing a free sulfhydryl; and (iii) (optional) a disulfide bond formation with a substrate protein (mainly with only one Cys protein) through nonactivity-based cross-linking or with a deubiquitinase (mainly with several Cys residues) through activity-based cross-linking. When the bifunctional molecule and target proteins are obtained, the final products can be prepared in milligram quantities within 2 weeks.
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; Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including Wireless Sensor Networks (WSNs). Existing algorithms are primarily employed to detect events such as objects or physical chan...
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; Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including Wireless Sensor Networks (WSNs). Existing algorithms are primarily employed to detect events such as objects or physical changes at a high, low, or fixed frequency sampling usually adapted by a central unit or a sink, therefore requiring additional resource usage. Additionally, this algorithm potentially makes a network unable to capture a dynamic change or event of interest, which therefore affects monitoring quality. This article studies the problem of a fully autonomous adaptive sampling regarding the presence of a change or event. We propose a novel scheme, termed "event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)" by addressing the problem in two stages, which leads to reduced resource usage (e.g., energy, radio bandwidth). First, e-Sampling provides the embedded algorithm to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage, while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained networks. In the absence of an event, the "uninteresting" data is not transmitted to the sink. Thus, the energy cost is further reduced. e-Sampling can be useful in a broad range of applications. We apply e-Sampling to Structural Health Monitoring (SHM) and Fire Event Monitoring (FEM), which are typical applications of high-frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in effectively expanding the capacity of WSNs for high data rate applications.
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