With the increasing requirement for computational efficiency in evolutionary algorithms (EAs) when tackling many optimization tasks concurrently, many-task optimization (MaTO) has gained much attention in recent years. It involves extracting and transferring the knowledge from successful search experiences across many tasks. As the number of tasks and variable scale grows in MaTO, the need for efficient knowledge transfer in EAs becomes indispensable. In this article, we present a task-averaged natural gradient-assisted natural evolution strategy (TNG-NES) to deal with MaTO efficiently. The task-averaged natural gradient (TNG) captures how a group of task distributions evolves, considering their overall trends of mean and covariance. This can accelerate the optimization process across all tasks by leveraging the evolutionary similarities among multiple task search distributions. Notably, TNG-NES exhibits linear computational complexity concerning the number of tasks for knowledge transfer. Additionally, to adaptively utilize TNG for distribution evolution and mitigate negative transfer, we introduce a transfer adaptive control mechanism for TNG-NES. We conducted extensive experiments on CEC19-MaTO, WCCI22-MaTO, our proposed large-scale MaTO benchmark suite, and real-world applications. The results validate the effectiveness of TNG-NES, outperforming state-of-the-art MaTO algorithms.

Competitive multiobjective multitask optimization (CMO-MTO) problems involve multiple tasks with comparable objectives but heterogeneous decision variables. The final Pareto front in CMO-MTO consists of multiple subsets corresponding to different tasks. Since the Pareto front subset of one task may be dominated by that of another, competition arises among tasks. Additionally, there may be exploitable similarities among tasks that evolutionary multitasking methods can leverage. For a comprehensive study of CMO-MTO, we construct 12 benchmark CMO-MTO problems with varied competitive relationships and inter-task similarities. To effectively solve CMO-MTO problems, we propose a reference vector contribution-based multitask evolutionary algorithm (RVC-MTEA). RVC-MTEA facilitates both global and local knowledge transfer based on vector contributions and integrates global archives to gather non-dominated solutions across multiple competitive tasks. Comparative results with four popular single-task and six state-of-the-art multitask evolutionary algorithms demonstrate the efficacy of RVC-MTEA. Finally, we apply RVC-MTEA to several real-world applications, showcasing the potential of CMO-MTO in practical decision-making scenarios.

Multiobjective multitask optimization (MO-MTO) aims to exploit the similarities among different multiobjective optimization tasks through knowledge transfer (KT), facilitating their simultaneous resolution. The effective design of KT techniques embedded in multiobjective evolutionary optimizers is crucial for enhancing the performance of multiobjective multitask evolutionary algorithms (MO-MTEAs). However, a significant limitation of existing KT techniques in MO-MTEAs is their equal treatment of particles/individuals for transferred knowledge reception, which can negatively impact the balance of diversity and convergence in population evolution. To remedy this limitation, this article proposes a new MO-MTEA, named MTEA-DCK, which incorporates diversity-oriented KT (DKT) and convergence-oriented KT (CKT) techniques tailored for different particles in the population. MTEA-DCK utilizes a strength-Pareto-based competitive mechanism to divide particles into winners and losers: 1) for winners, DKT is conducted via an intertask domain alignment approach to enhance population diversity and 2) for losers, CKT is executed within the unified search space to improve convergence. Additionally, to ensure robust performance on complex task combinations, we introduce two automatic parameter control strategies specifically designed for these KT techniques. MTEA-DCK was performed on 39 benchmark MO-MTO problems and demonstrated superior performance compared to eight state-of-the-art MO-MTEAs and six multiobjective evolutionary algorithms. Finally, we present three real-world MO-MTO application cases, where our approach also yielded better results than other algorithms.

Evolutionary multitasking has attracted much attention over the past years due to its inter-task knowledge transfer capability. In this area, multiobjective multitask optimization (MO-MTO), aims to handle multiple multiobjective optimization tasks faster and better simultaneously via population synergies among tasks. Existing multiobjective multitask evolutionary algorithms (MO-MTEAs) for MO-MTO mostly transfer positions, i.e., decision variables, which may invoke negative knowledge transfer on tasks with low optimal domain similarities. However, such low similarities are common in practice. To address this issue, this paper proposes a new MO-MTEA, named MTEA/D-TSD, which transfers search directions, rather than positions, among decomposed subtasks for MO-MTO. In addition to position-neighborhood in the decomposition method, MTEA/D-TSD constructs and adaptively updates the search-direction-neighborhood for each subtask. It transfers successful search directions among neighbor subtasks to accelerate population evolution. Moreover, to further improve the efficiency of knowledge transfer, a transfer rate self-adaptation strategy is designed for MTEA/D-TSD. Experimental results on MO-MTO benchmark problems and a real-world application of sensor coverage problems demonstrated the superior performance of MTEA/D-TSD against state-of-the-art MO-MTEAs.

Spectrum sharing is a key technology to relieve the ever-increasing spectrum demand and realize the full potential of radio spectrum. In this paper, we study spectrum sharing between higher priority users and lower priority users under geo-location based spectrum sharing systems. We consider a dynamic spectrum allocation scheme that allocates spectrum to a higher priority user based on its spectrum need that can be determined by its traffic load. The lower priority users utilize the unallocated spectrum. In addition to studying the performance of higher priority users with this dynamic spectrum allocation scheme, we also investigate the impact of this scheme on spectrum availability and stability of lower priority users. We develop a mathematical model to analyze the performance. The simulation results indicate that spectrum sharing is efficient, and the spectrum is abundant and relatively stable to lower priority users, even when the system is moderately loaded with higher priority users.

Advances in mobile Internet technology have enabled the clients of Web services to be able to adjust to context changes, which need to rely on monitoring to QoS of Web services. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the complicated relations among multi-dimension of context. This paper proposes a learning approach to quality-of-service (QoS) prediction of web services via multi-dimensional context derived from the past invocation history. To validate our approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than other approaches.

Software reuse through Application Programming Interfaces (APIs) is a common practice in software development. It remains a big challenge to bridge the semantic gap between user expectations and application functionality with the development of Web-based services. This paper proposes a service recommendation approach via extracting semantic relationship from natural language API descriptions and inferring. To validate our approach, large-scale experiments are conducted based on a real-world accessible service repository, ProgrammableWeb. The results show the effectiveness of our proposed approach.

UNLABELLED: : Identifying chemical probes or seeking scaffolds for a specific biological target is important for protein function studies. Therefore, we create the Annotated Scaffold Database (ASDB), a computer-readable and systematic target-annotated scaffold database, to serve such needs. The scaffolds in ASDB were derived from public databases including ChEMBL, DrugBank and TCMSP, with a scaffold-based classification approach. Each scaffold was assigned with an InChIKey as its unique identifier, energy-minimized 3D conformations, and other calculated properties. A scaffold is also associated with drugs, natural products, drug targets and medical indications. The database can be retrieved through text or structure query tools. ASDB collects 333 601 scaffolds, which are associated with 4368 targets. The scaffolds consist of 3032 scaffolds derived from drugs and 5163 scaffolds derived from natural products. For given scaffolds, scaffold-target networks can be generated from the database to demonstrate the relations of scaffolds and targets. AVAILABILITY AND IMPLEMENTATION: ASDB is freely available at http://www.rcdd.org.cn/asdb/with the major web browsers. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interactivity in transport protocol can greatly benefit transport friendly applications. We envision if a transport mechanism, which is interactive and can provide event notification about network state to the subscriber of its communication service, than a wide range of solution to many of today's hard network problem can be instituted. Recently we have implemented this concept system as a new TCP kernel on FreeBSD called TCP interactive and a novel symbiotic MPEG-2 full logic transcoder. In this paper we share the results of the TCP interactive performance experiment and show potential dramatic improvement in time-bounded video delivery.

In this paper we discuss architecture for network aware adaptive systems for next generation networks. We present in the context of a novel cognizant video transcoding system, which is capable of negotiating local network state based rate and let the video propagate over extreme network with highly asymmetric link and node capacities utilizing knowlege about the network, content protocol and the content itself.

The authors present a potential new approach of application integrated congestion management. It is applicable for time-sensitive traffic and is based on the principle of direct protocol interactivity. In contrast to classical transport protocols, we envision a transport mechanism which is interactive and can provide event notification to the subscriber of its communication service. We then show a network aware adaptive MPEG-2 video transcoding mechanics, which directly interacts with the protocol and adjusts its production in sync with the impairment events in the transport layer. The authors present the novel symbiotic mechanics, and share the performance result observed on the MPEG-2 video stream carried by this symbiosis implementation. We report dramatic improvement in time-conformant video delivery by such protocol interactivity.