We live in an era of major societal and technological changes. Post-industrial demographic trends, like massive migrations and an ageing society, and transportation de-carbonization generate new challenges for cities making an efficient and sustainable management of services and resources more than ever necessary. Cities must evolve, transform and become “Smart Cities” to cope with these challenges. According to the literature, a city can be referred to as ‘smart’ “when investments in human and social capital and traditional (transportation) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory government”. A Smart City is a city’s response to the challenges referred above, but also the opportunities raised by the societal digitalization and the evolution and progress of Information and Communications Technologies (ICT). Ubiquitous sensing and actuation devices do not make a city smart by themselves; it is rather a matter of the city’s structure, dynamics, and the implications of the technology on the improvement of the wealth and quality of life of its citizens.

Hence, the challenge for smart cities, and smart mobility in particular is twofold: first, to provide access to all the traditional services a city needs to provide citizens in this context of rising population concentration in such cities. Second, to make the city a more sustainable urban ecosystem, taking advantage of the moment of redefinition of cities, and the context of technological development, which translates into a greener and more efficient mobility.

Indeed, a central service affecting many of the others (economic development, access to health and education, etc.) is mobility. Concrete benefits can be seen, for instance, in Public Transportation. A more accurate demand profiling — by virtue of e.g. crowd-sourced sensing applications (smart apps) — may help both service providers to plan optimal routes, and periodicities, and travelers, with a service closer to their daily needs. Privately owned vehicles can also take advantage of a holistic smart city strategy, while contributing to mobility management, for example by sharing their daily commuting traces and obtaining in return more accurate travel time predictions and alternative optimal routes from the city. An important aspect in Smart Cities and Smart Mobility is its bi-directionality, as can be seen in these examples.

Regarding logistics in the business world, connections have been considerably improved during the last decades. Thanks to this, an efficient logistic network can make a huge difference for companies and relevant business operations. Consequently, and due to the rapid advance of technologies, different innovative problems related with the design and solving of smart mobility and logistic situations have arisen in the literature. This fact has given to these fields a momentum in the current scientific community.

Mostly all across smart mobility shares a common challenge: limited resources to be optimized. The main reasons for the popularity and importance of these optimization problems are two folded: the social interest they generate, and their inherent scientific interest. On one hand, routing problems are normally designed to deal with real-world situations related to the transportation or logistics. This is the reason why their efficient resolution entails a profit, either social or business one. On the other hand, most of the problems arising in this field have a great computational complexity. For this reason, the resolution of these problems is a major challenge for the scientific community.

This special issue, that will be published in IEEE Intelligent Transportation Systems Magazine, aims at disseminating the latest findings, research achievements and ideas through the eye of data-driven optimization, with an intention to balance between theoretical research ideas and their practicability as well as industrial applicability. Special attention will be paid to data-driven optimization algorithms and techniques, proving their use in smart city and transportation logistics applications.

Topics of interest include, but are not limited to:

  • Novel Distributed Optimization methods, including cellular and island-based schemes.
  • Swarm Intelligence, evolutionary computation and other nature-inspired optimization techniques.
  • Meta- and Hyper-heuristics for optimization.
  • Hybridization of Swarm Intelligence techniques, Memetic Computing, Adaptive Swarm Intelligence methods.
  • Multi-objective and many-objective solvers.
  • Online optimization based on real time traffic sensing data.

Applied to the following fields (not limited):

  • Parking lot optimization and demand management.
  • Emergency and public transportation aware traffic planning.
  • Environmentally friendly mobility planning.
  • Federated and collaborative logistics.
  • Transportation sharing tasks.
  • Pedestrian dynamics.
  • Complex traffic planning and optimization using crowdsourced data in urban environments.
  • Car sharing and carpooling.

Submitted papers should be original and are not be under consideration elsewhere for publication. The authors should follow the journal guidelines, regarding the manuscript content and its format when preparing their manuscripts. All papers will be reviewed by at least three independent reviewers for their suitability in terms of technical novelty, scientific rigor, scope, and relevance to this special issue. Interested researchers are invited to submit their unpublished work via the submission system https://mc.manuscriptcentral.com/itsm

Important dates:

  • November 15, 2018: Deadline for Initial Paper Submission.
  • January 10, 2019: Notification of First Round Decision.
  • February 7, 2019: Deadline for Revised Paper Submission.
  • March 1, 2019: Final acceptance decision.
  • April 1, 2019: Deadline for Final paper submission.

Guest editors

  • Dr. Eneko Osaba, TECNALIA, Zamudio, Spain. eneko.osaba@tecnalia.com.
  • Prof. Dr. Javier J. Sanchez-Medina, Computer Science Department, University of Las Palmas de Gran Canaria. javier.sanchez.medina@ieee.org.
  • Prof. Dr. Javier Del Ser, University of the Basque Country (UPV/EHU), Basque Center for Applied Mathematics (BCAM), Bilbao, Spain, and TECNALIA, Zamudio, Spain. javier.delser@ehu.es.
  • Dr. Joshué Perez, TECNALIA, Zamudio, Spain. joshue.perez@tecnalia.com.
  • Prof. Dr. Xin-She Yang, School of Science and Technology, Middlesex University, Hendon Campus, London, NW4 4BT, United Kingdom. X.Yang@mdx.ac.uk.
  • Dr. Antonio D. Masegosa, Faculty of Engineering, University of Deusto, 48007, Bilbao, Spain, and IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain. ad.masegosa@deusto.es.
  • Prof. Dr. Eleni I. Vlahogianni, Department of Transportation Planning and Engineering, National Technical University of Athens, 157 73 Athens, Greece. elenivl@central.ntua.gr.

For further information, please, download the Call for Papers, or refer to the webpage of the conference (available soon).


Special Session on Swarm Intelligence and Swarm Robotics


The Special Session Swarm Intelligence and Swarm Robotics to be held during the 15th International Conference on Distributed Computing and Artificial Intelligence (DCAI2018) will gather researchers and practitioners to foster and ease rich discussions around the latest findings, research achievements and ideas in the areas of Swarm Intelligence and Swarm Robotics. Interested colleagues are invited to submit novel contributions via the submission system with an emphasis on the following topics (but not limited to):

  • Recent advances on Swarm Intelligence methods for Robotics, with emphasis on those inspired by biological processes and behaviors typically observed in Nature, such as Particle Swarm Optimization, Bat Algorithm, Cuckoo Search, Ant Colony Optimization, Artificial Bee Colony, Firefly Algorithm and others.
  • Novel applications of Swarm Intelligence to Robotics, with an emphasis on real-world scenarios.
  • Hybridization of Swarm Intelligence and Soft Computing techniques, with applications to robotics and autonomous complex systems.
  • New synergies between Swarm Intelligence and Swarm Robotics.
  • Coordination and control of Swarm Robotic Systems.
  • Adaptive Swarm Intelligence methods.
  • Applications of Swarm Intelligence for collaborative positioning and route optimization in robotic swarms.
  • Distributed inference in Swarm Robotics.
  • Self-organization in robotics enabled by Swarm Intelligence.
  • Distributed Swarm Robotic systems.

Only submissions with original contributions with respect to the state of the art in the above areas will be considered for inclusion in this session, i.e. special session papers will be treated under the same criteria as regular conference papers.

DCAI 2018 is sponsored by the IEEE Systems Man and Cybernetics Society Spain Section Chapter and the IEEE Spain Section. Accepted papers will be included in DCAI Proceedings. At least one of the authors will be required to register and attend the symposium to present the paper in order to include the paper in the conference proceedings. All accepted papers will be published by AISC series of Springer Verlag.

Important dates:

  • Paper submission deadline: 5th February, 2018.
  • Notification of acceptance: 12th March, 2018.
  • Camera-Ready papers due: 26th March, 2018.
  • Conference Celebration: 20th-22nd June, 2018.

For further information, please, download the Call for Papers, or refer to the webpage of the conference.


Virtual Special Issue on New Paradigms, Trends and Applications of Machine Learning and Soft Computing in Cyber-Physical Systems

For this Virtual Special Issue in Applied Soft Computing, authors are invited to submit their original works focusing on how Cyber-Physical Systems can benefit from its synergy with Soft Computing techniques and Machine Learning models, with an emphasis on evidences of the practicability of the reported findings. Topics of interest for this special issue include, but are not limited to, the following:

  • Novel Soft Computing techniques and their application to problems related to CPS, such as distributed predictive modelling, hybrid optimization techniques, online learning over data streams, concept drift adaptation, automated model construction, large-scale deployment of Soft Computing techniques, collaborative reasoning and weakly/semi-supervised learning, among others
  • Data analytics and scalable/parallel/distributed computing algorithms for CPS
  • Artificial Intelligence (AI) as a service (AIaaS) for CPS
  • Energy efficiency paradigms for CPS tackled via Soft Computing and Machine Learning
  • Distributed computing, data fusion and aggregation over large-scale CPS
  • Predictive and clustering models for CPS self-configuration, self-resilience and self-autonomy
  • Optimization algorithms for optimal sensor actuation
  • Autonomic computing, inference of human patterns, analysis, monitoring, and situation alertness in CPS
  • Federated learning, collaborative machine learning and distributed AI for large scale CPS
  • Soft Computing techniques to enable ultra-reliable, low-latency applications in CPS scenarios.

For further information about the deadlines or the editorial board, please, check the journal’s S.I. webpage.



12th International Symposium on Intelligent Distributed Computing (IDC 2018)

The 12th International Symposium on Intelligent Distributed Computing (IDC 2018) will be held in October 2018 in Bilbao, Spain. The main goal of these series of symposiums is to gather researchers and practitioners to foster and ease rich discussions around the latest findings, research achievements and ideas in the area of Intelligent Distributed Computing. The IDC provides an open forum for enhancing the collaboration between researchers, lecturers, and students from Intelligent Computing and Distributing Computing communities. Intelligent Computing covers a hybrid palette of methods and techniques ranging from classical artificial intelligence, information sciences or computational intelligence to more recent trends such as swarm intelligence, bio-inspired computation, cloud computing or machine learning. Distributed Computing develops technologies and methods to build complex computational systems composed of collaborating software components spread over different computational elements. Recent trends on this field can be the ephemeral computing, federated learning or swarm robotics. Thus, the field of Intelligent Distributed Computing seeks for the design and implementation of new generation of intelligent distributed systems, adapting or hybridizing researches in both Intelligent Computing and Distributed Computing. IDC 2018 welcomes research works centered on all aspects of intelligent distributed computing, with an intention to balance between theoretical research ideas and their practicability as well as industrial applicability. To this end, scholars and practitioners from academia and industrial fields are invited to submit high-quality original contributions to IDC 2018. The structure of the symposium consists of regular sessions with technical contributions reviewed and selected by an international program committee, as well as of special sessions focused on multi-disciplinary and cutting-edge topics.

IDC 2018 has a special interest in (but will not be limited to) novel architectures, systems and methods that facilitate distributed / parallel / multi-agent biocomputing for solving complex computational and real-life problems. Symposium topics include, but are not limited to:

Intelligent Distributed and High-Performance Architectures

  • Hybrid distributed systems involving software agents and human actors
  • Intelligent grid and cloud infrastructures
  • Agent-based wireless sensor networks
  • Distributed frameworks and middleware for the Internet of Things
  • GPU, multicore, and many-core intelligent computing
  • Intelligent high-performance architectures
  • Context-aware intelligent computing
  • Virtualization infrastructures for intelligent computing
  • Bio-inspired and nature-inspired distributed computing

Organization and Management

  • Autonomic and adaptive distributed computing
  • Intelligent service composition and orchestration
  • Self-organizing and adaptive distributed systems
  • Emerging behaviors in complex distributed systems
  • Intelligent integration of heterogeneous data and processes
  • Methodologies for development of intelligent distributed systems and applications

Ehemeral and Unreliable computing

  • Theory and applications of complex ephemeral or unreliable environments
  • Design and deployment of ephemeral computing systems
  • Application of Soft Computing methods on computational environments featuring ephemeral behavior (unreliability, dynamicity, and/or heterogeneity)
  • Meta-heuristics for modeling and analyzing systems with ephemeral properties, such as social network dynamics, ephemeral clustering and pattern mining, ephemeral computational creativity or content generation.

Intelligent Distributed Knowledge Representation and Processing

  • Information extraction and retrieval in distributed environments
  • Knowledge integration and fusion from distributed sources
  • Data mining and knowledge discovery in distributed environments
  • Semantic and knowledge grids
  • Ontologies and meta-data for describing heterogeneous resources and services
  • Distributed fusion of sensor data streams
  • Big Data Processing

Networked Intelligence

  • E-service and web intelligence
  • Intelligence in mobile, ubiquitous and wearable computing
  • Intelligence in peer-to-peer systems
  • Intelligence in distributed and networked multimedia systems
  • Security, privacy, trust and reputation

Parallel metaheuristics for optimization

  • Global single-population master-slave, panmictic population GAs
  • Single-population fine-grained GAs
  • Multiple-population, multiple-deme, distributed, island based, coarse grained, GAs
  • Evolutionary simulated annealing, Distributed Tabu Search
  • Parallel Variable Neighborhood Search
  • Swarm intelligence methods based on distributed knowledge sharing: bat algorithm, firefly algorithm, imperialist competitive algorithm, particle swarm optimization, ant colony optimization, artificial bee colony, golden ball, coral reefs optimization, water cycle algorithm, cuckoo search, harmony search…
  • Hybridization of Swarm Intelligence techniques, Memetic Computing, Adaptive Swarm Intelligence methods
  • Distributed Evolutionary Techniques, Cellular Evolutionary Algorithm, Hyper Heuristics
  • Applications of swarm intelligence techniques for distributed or cooperative environments

Distributed swarm robotics systems

  • Recent advances on Soft Computing methods for Robotics
  • Novel applications of Swarm Robotics, with a priority on real-world scenarios
  • New synergies between Swarm Intelligence and Swarm Robotics
  • Coordination and control of Swarm Robotic Systems
  • Applications of Swarm Intelligence for collaborative positioning and route optimization in robotic swarms
  • Distributed inference in Swarm Robotics
  • Self-organization in robotics enabled by Swarm Intelligence
  • Nature inspired methods for data science and machine learning
  • Recent advances on nature inspired methods for data science
  • Novel applications of bio-inspired methods to data mining, with priority on real-world scenarios

Nature-inspired methods for supervised and unsupervised data mining

  • Hybridizing bio-inspired methods with machine learning and data mining techniques
  • Nature-inspired methods for feature selection and/or instance generation/selection
  • Implementation of bio-inspired methods using Big Data technologies
  • Federated learning: theory and applications

Intelligent Distributed Applications

  • Distributed problem solving and decision making
  • Intelligent applications in e-business/e-commerce, e-learning, e-health, e-science, e-government, crisis management, smart grid
  • Modeling, simulation and development of intelligent distributed systems
  • Simulation of groups and crowds
  • Intelligent data processing
  • Intelligent robots

Special Issue on Soft Computing for Swarm Robotics: New Trends and Applications

This Special Issue in the Journal of Computational Science aims at disseminating the latest findings and research achievements in the areas of Swarm Intelligence and Swarm Robotics, with an intention to balance between theoretical research ideas and their practicability as well as industrial applicability. Swarm Intelligence (SI) refers to the complex collective behavior of self-organized and decentralized systems, typically composed of a (spatially distributed and often large) population of individuals, or agents. These agents interact among them and with the environment in different but simple and local ways, coordinating their actions, and making the swarm inherently robust, effective, and flexible. A plethora of application scenarios have hitherto resorted to SI when addressing optimization, inference and prediction tasks. Among them, Swarm robotics (SR) refers to the application of SI methods to scenarios where the population of agents is embodied by physical or simulated robotic devices. The focus of SR is to thoroughly analyze how a swarm comprised of relatively simple physically embodied robots can be controlled to collectively accomplish different kind of goals that are out of the common capabilities of a single robot. Algorithms and methods relying on SR have been so far exceled over a wide range of complex real-world problems, such as localization, mining, disaster rescue missions, agricultural foraging or scenery mapping problems. The interests in SR form a popular topic that lays at the core of many research activities and contributions in the literature.

For further information about the deadlines or the editorial board, please, check the Call for Papers, or the journal’s S.I. webpage.


Upcoming book: Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-Inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. In this regard many different inspirational sources can be found for these solvers, such as the behavioral patterns of bats, fireflies, corals, bees or cuckoos, as well as the mechanisms behind genetic inheritance, musical harmony composition or bacterial foraging. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics to be covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, adaptive nature-inspired techniques, hyper heuristics, memetic methods, or distributed evolutionary techniques, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization, dynamic and constraint satisfaction problems. This material unleashes a great opportunity for researchers, lecturers, and practitioners interested in nature-inspired computation, artificial intelligence and optimization problems.

More information about the book can be found in its call for papers. Please, if you want to be informed about the upcoming updates, contact us!


Special Issue on New Theoretical Insights and Practical Applications of Bio-inspired Computation Approaches

This special issue to be published in Swarm and Evolutionary Computation gravitates on a two-fold scope: on one hand, the issue will consider technically sounding contributions around the latest theoretical findings on the convergence properties of new bio-inspired optimization and machine learning techniques. Contributions resorting to solid mathematical frameworks and theoretical developments will be specially welcome.

On the other hand practical applications leveraging the computational benefits of bioinspired techniques will be also considered for publication. Bio-inspired techniques are widely acknowledged to tackle complex optimization and learning paradigms in a computationally efficient manner. This special issue will solicit exemplifying research works dealing with the application of bioinspired approaches to problems emerging from different disciplines, with a particular emphasis on those under the Big Data thematic umbrella.

For further information, please, check the call for papers webpage.