2016 IEEE Congress on Evolutionary Computation (CEC 2016)

Special Session on ¡°Fireworks Algorithm and Its Application on Big Data¡±


1 Aim and Scope

Big data contains large information and is worth researching. Big data, also known as massive data or mass data, referring to the amount of data involved that are too great to be interpreted by a human. The Obama administration invested nearly two hundred million US dollars on the program of "Big Data Research and Development Initiative", aiming to protect the national security. In addition, sociologists use the big data from social interaction network to analyze the human behavior, communication methods.

However, the methods to process big data are ineffective. Currently, the suitable technologies include A/B testing, crowdsourcing, data fusion and integration, genetic algorithms, machine learning, natural language processing, signal processing, simulation, time series analysis and visualization. But real or near-real time information delivery is one of the defining characteristics of big data analytics. It is important to find new methods to enhance the effectiveness of big data.

Fireworks algorithm (FWA) achieved great success on solving many complex optimization problems effectively. FWA has a unique search manner in the solution space and is a strong capability to solve optimization problems. It has many effective variants and huge amount of successful applications. Moreover, FWA is suitable for parallelization and works significantly better than other SI algorithms, such as particle swarm optimization, ant colony optimization and genetic algorithm.

The main aim of this special session is to bring together both experts and new-comers from either academia or industry to discuss fireworks algorithm and its application, especially on big data. However, both the improvement of FWA and the application of FWA are acceptable for this special session.

Scope and Topics
Full papers are invited on recent advances in the development of FWA, i.e., FWA improvements and applications. In addition, we are interested in various studies discussing processing big data issues by FWA. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:

  • Theoretical analysis of FWA
  • Algorithmic improvement of FWA
  • FWA for single-, multi-, and many-objective optimization
  • FWA for data mining and machine learning
  • FWA for big data and data analysis
  • Parallelized and distributed realizations of FWA
  • Applications of FWA in big data
  • FWA for all the other applications

2 Important Dates

Authors are welcome to submit original, latest, and unpublished contributions to the Special Session by using the online submission system at the CEC 2016 website at http://www.wcci2016.org/.

Please notice

  • Paper submission: 16 Jan 2016.
  • Conference dates: 25-29 July 2016.

3 Submission

Please visit the forum of FWA http://www.cil.pku.edu.cn/research/fwa/index.html before your submission.

When submitting their manuscripts, authors are recommended to follow these steps:

  • Identify the conference associated to the Special Session they are interested in, by looking at the ¡°Provisionally Accepted Special Session¡± list under the column called ID;
  • Go to the conference submission website http://www.wcci2016.org/submission.php;
  • Select the Special Session name in the Main Research topic dropdown list;
  • Fill out the input fields, upload the pdf file and finalize the submission by Janurary 16, 2016.

4 Special Session Co-Chairs


  • Prof. Ying Tan, School of Electronics Engineering and Computer Science, Peking University, Beijing, China, Email: ytan@pku.edu.cn
  • Associate Prof. Liangjun Ke, Systems Engineering Institute, Xi'an Jiaotong University, Shaanxi, China, Email: kelj163@163.com
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