SI 2019 - Special Issue on: "NETWORK OPTIMIZATION PROBLEM THROUGH EVALUTIONARY ALGORITHM”

Special Issue on: "NETWORK OPTIMIZATION PROBLEM THROUGH EVALUTIONARY ALGORITHM”
http://airccse.org/journal/sicfp1-19.html
Guest Editors:
Arindam Biswas
Asansol Engineering College, India.
Amit Banerjee
National University of Singapore, Singapore.
Gorachand Dutta
University of Bath, United Kingdom.
Jintendra Nath Roy
Kazi Nazrul University, India.
Theme and Scope
Network optimization is basically a fundamental issue in various fields, including applied mathematics, computer science, engineering, management, and operations research. Network models provide a useful way for modeling various real world problems and are extensively used in many different types of systems: communications, mechanical, electronic, manufacturing and logistics. However, many practical applications impose on more complex issues, such as complex structure, complex constraints, and multiple objectives to be handled simultaneously and make the problem intractable to the traditional approaches
Recent advances in evolutionary algorithms (EAs) focus on how to solve such practical network optimization problems. EAs are stochastic algorithms whose search strategies model the natural evolutionary phenomena; genetic inheritance and Darwinian strife for survival. Usually it is necessary to design a problem-oriented algorithm for the different types of network optimization problems according to the characteristics of the problem to be treated. Therefore, how to design efficient algorithms suitable for complex nature of network optimization problems is the major focus. Generally, EAs involve following meta heuristic optimization algorithms, such as genetic algorithm (GA), evolutionary programming (EP), evolution strategy (ES), genetic programming (GP), learning classifier systems (LCS), and swarm intelligence (comprising ant colony optimization: ACO and particle swarm optimization: PSO). Beside these, nowadays several new algorithms have also developed like Bacteria foraging optimization (BFO), Ant lion optimization (ALO) etc
Topics of interest
• Network Optimization through genetic algorithm (GA), evolutionary programming (EP), evolution strategy (ES), genetic programming (GP), learning classifier systems (LCS), and swarm intelligence (comprising ant colony optimization: ACO and particle swarm optimization: PSO). Beside these, nowadays several new algorithms have also developed like Bacteria foraging optimization (BFO), Ant lion optimization (ALO) etc.
• About network problems, their design and mathematical analysis, as well as efficient algorithms for carrying out optimization on networks
• The nonstandard modeling of diverse processes using networks and network optimization concepts
• Studying networks including applied mathematics, operations research, computer science, discrete mathematics, and economics.
• The analytic modeling of problems using networks, the mathematical analysis of network problems, the design of computationally efficient network algorithms, and innovative case studies of successful network applications.
• The realm of pure graph theory (without significant algorithmic and modelling    contributions) or papers that deal with engineering aspects of network design and  optimization
We seek to publish original, well-written research papers that make a substantive contribution to the knowledge base. In addition, tutorial and survey articles are welcomed. All manuscripts are carefully refereed.
Notes for Prospective Authors
Authors are invited to submit papers for this SI through e-mail sinop-AT-airccse.org Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this SI
Important Dates
• Submission Deadline : November 17, 2018
• Notification : December 31, 2018
• Final manuscript due : January 15, 2019
• Announcement of acceptance by the Guest Editors: January 30, 2019
• Final manuscripts due: January 30, 2019
For more details please visit: http://airccse.org/journal/ijcnc.html