Mathematical Problems in Engineering

Swarm Intelligence in Engineering 2014

Publishing date
24 Oct 2014
Submission deadline
06 Jun 2014

1Dalian University of Technology, Dalian, China

2Jilin University, Changchun 130022, China

3Dalian Maritime University, Dalian 116024, China

4Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands

Swarm Intelligence in Engineering 2014


Swarm intelligence (SI) is an artificial intelligence technique based on the study of behavior of simple individuals (e.g., ant colonies, bird flocking, animal herding, and honey bees) in various decentralized systems. The population, which consists of simple individuals, can usually solve complex tasks in nature by individuals interacting locally with one another and with their environment. Although their behaviors are primarily characterized by autonomy, distributed functioning, and self-organizing capacities, local interactions among the individuals often cause a global optimal.

Recently, SI algorithms have attracted much attention of researchers and have also been applied successfully to solve optimization problems in engineering. However, for large and complex problems, SI algorithms consume often much computation time due to stochastic feature of the search approaches. Therefore, there is a potential requirement to develop efficient algorithm to find solutions under the limited resources, time, and money in real-world applications.

The aim of this special issue is to highlight the most significant recent developments on the topics of SI and to apply SI algorithms in real-life scenario. Contributions containing new insights and findings in this field are welcome. Particular attention will be given to the following theme areas; however, it should be stressed that a broad range of submissions are encouraged. Potential topics include, but are not limited to:

  • Benchmarking and evaluation of new SI algorithms
  • Convergence proof for SI algorithms
  • Comparative theoretical and empirical studies on SI algorithms (e.g., ant colony optimization, particle swarm optimization, artificial bee swarm algorithm, bacterial foraging optimization, and artificial fish algorithm)
  • SI algorithms for real-world applications (e.g., aerospace engineering, bioengineering, chemical engineering, computer engineering, electrical engineering, industrial engineering and manufacturing systems, and mechanical engineering)

Before submission authors should carefully read over the journal's Author Guidelines, which are located at Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at according to the following timetable:

Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
Impact Factor1.009