Two special issues are dedicated to the IEEE Workshop PDCO 2019.
First Special issue: Swarm and Evolutionary Computation journal (Elsevier, IF: 6.33).
https://www.journals.elsevier.com/swarm-and-evolutionary-computation/call-for-papers/special-issue-on-paralleldistributed-combinatorics
Special Issue on Parallel/Distributed Combinatorics and Optimization
The increasing size and complexity of optimization problems has motivated the usage of parallel computing to provide high-quality solutions in reasonable time. On the other hand, parallel computing recent evolution (e.g., multi-core and many-core processors) has made it more affordable while being more performant. In order to efficiently exploit such computing environments, novel parallel/distributed optimization paradigms have to be designed and new implementations provided. The purpose of this special issue is to collect the main recent trends and designs in parallel and distributed combinatorics and optimization methods for solving hard optimization problems. Topics of interests include (but are not limited to):
• Global optimization, combinatorial optimization, multi-objective optimization, dynamic optimization;
• Computational intelligence methods (e.g., evolutionary algorithms, swarm intelligence, ant colonies, cellular automata, DNA and molecular computing);
• Cooperative/competitive methods;
• Hybrid algorithms;
• Peer-to peer computing and optimization problems;
• Real world applications: e.g., cloud computing, planning, logistics, manufacturing, finance, telecommunications.
All high quality submitted papers related to the listed topics will be considered for publication in this special issue, provided they are recommended for publication after the review process.
The organization of this special issue is linked to the 9th IEEE Workshop Parallel / Distributed Combinatorics and Optimization (PDCO 2019), that took place together with the 33rd IEEE International Parallel and Distributed Symposium (IPDPS 2019) but it is not restricted to papers from the PDCO workshop.
Papers should be submitted using SWEVO's online submission system: https://www.journals.elsevier.com/swarm-and-evolutionary-computation. When submitting your manuscript please select the article type "VSI: PDCO".
IMPORTANT DATES
- Full Paper Regular Submission Due: October 15th, 2019
- Notification of Results: December 15th, 2019
- Revisions Due: February 15th, 2020
- Notifications of Final Acceptance: May 15th, 2020
- Submissions of Final Revised Papers: June 15th, 2020
- Expected publication date: 4th quarter of 2020
GUEST EDITORS:
Second Special issue: Future Generation Computer Systems journal (Elsevier)
Special issue on New Computing Paradigms of Stream Data Mining and Optimization in Non-Stationary Environments.
Strict deadline for paper submission: May 15, 2019. CFP
1. Summary and Scope
Lately the number of application scenarios where fast data streams are produced with varying characteristics along time is growing at a fast pace over very diverse sectors, particularly in industrial systems (prognosis), health (condition monitoring, anomaly detection), telecommunications (ultra-fast resource allocation, fraud detection) and security (intrusion detection over high-speed communication networks) among many others. In these scenarios, data may come from devices, sensors, web sites, social media feeds, applications, and other data-intensive infrastructures and processes alike, hence they are often noisy, heterogeneous in nature and evolve over time. In this context, real-world applications require to deal with changing environments, e.g., the estimation of the best route for a fleet of transport vehicles may depend on eventual traffic jams, weather broadcast and/or the state of the highway; job shop scheduling could depend on changing requirements in the manufacturing plant; market conditions in financial models are subject to news and media.
Such circumstances pose an urgent need for developing efficient computational models for data mining (clustering, classification/regression) and optimization not only to accommodate the high rates at which data streams are delivered, but also to adapt to changes in the conditions that ultimately impact on the patterns and solutions found by such models. These cases, often referred to as online/stream analytics where data mining and optimization models should operate efficiently on dynamic (close to real-time) environments, unchain complex design challenges in their learning algorithms, as many factors need to be jointly considered such as computational complexity, accuracy/optimality, flexibility of the model to adapt to new data distributions and/or time-varying scenarios, latency requirements, etc.
This research area is a merge of topics of interest to many disparate research communities. The novelty will reside initially in how to bridge the gap between tasks of interest to these different communities, by offering hybrid dynamic approaches that are able to efficiently ingest and analyse streaming data sources produced in nonstationary environments.
2. Topics:
This special issue focuses on such computational aspects and solicits articles dealing with online data processing models over streaming data, with an emphasis on descriptive analysis (including clustering), predictive modelling and optimization. Specifically, this special issue invites research papers to share latest research insights and present emerging results on theoretical and practical contributions related (but not limited) to:
• Dynamic optimization over time-evolving problem formulations.
• Multi-objective optimization and decision-making methods for nonstationary setups.
• Early classification over data streams.
• Semi-supervised/weakly-supervised predictive models for data stream mining.
• Unsupervised learning over data streams (e.g. clustering).
• Diversity-sensitive model construction for nonstationary concepts.
• Model adaptation to nonstationary datasets (e.g. concept drift).
• Change detection/classification approaches over evolving data streams.
• Design and validation of distributed online learning models and dynamic optimization solvers.
• Hybrid methods blending together elements from machine learning, heuristics and time series analysis.
• Computational complexity reduction strategies for learning models and optimization methods.
• New incremental models for learning/optimization.
• Model self-tuning approaches over data streams.
• Real-world applications of stream mining models and dynamic optimization solvers.
3. Submission Guidelines:
Original, high-quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences are sought. Papers will be peer-reviewed by independent reviewers and selected based on originality, scientific quality and relevance to this Special Issue. The journal editors will make final decisions about papers’ acceptance.
Authors should prepare their manuscript according to the Guide for Authors from the online submission system of Future Generation Computer Systems at the following website: http://www.evise.com/evise/jrnl/fgcs. Authors should select “VSI: CompStreamMiningOpt” when they reach the “Article Type” step in the submission process.
4. Important dates:
· Paper submission due: May 15th, 2019
· First-round acceptance notification: July 15th, 2019
· Revision submission: September 30th, 2019
· Notification of final decision: November 1st, 2019
· Submission of final paper: November 20th, 2019
· Publication date: 1st quarter of 2020
5. Guest editors:
· Prof. Dr. Javier Del Ser. TECNALIA, University of the Basque Country (UPV/EHU) and BCAM, Spain.
javier.delser@tecnalia.com
· Dr. Jose Manuel García-Nieto. University of Malaga, Spain
jnieto@uma.es
· Dr. Bernabé Dorronsoro. University of Cadiz, Spain Email:
bernabe.dorronsoro@uca.es
· Prof. El-Ghazali Talbi. University Lille, France Email:
El-ghazali.talbi@univ-lille1.fr
· Prof. Dr. Carlos Coello-Coello. CINVESTAV-IPN, Mexico
ccoello@cs.cinvestav.mx
