Special Issue "Bio-Inspired Evolutionary Computation and their Applications in Complex Adaptive Systems"

Submission Deadline: 15 June 2022 (closed)
Guest Editors
Dr. J. Alfred Daniel, Anna University, India.
Dr. Awais Ahmad, Università Degli Studi di Milano, Milan, Italy.
Dr. Boris Tomaš, University of Zagrebn, Croatia.
Dr. C Chandru Vignesh, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India.
Dr. M. Newlin Rajkumar, Anna University, India.

Summary

In this modern era, the inevitable advancement of technology plays an impeccable role in providing solutions for problems in science and engineering. However, finding optimum solutions for serious dimensionality problems relating to time is also difficult because of the behaviour of complex adaptive systems which is made up of various heterogeneous entities. Thus, interaction and performing actions for promoting desired behaviour and results becomes a difficult task. Many bio-inspired competing techniques such as co-evolution and reinforcement learning, dynamic programming, and swarm evolutions are used to optimize and solve control problems and find optimum solutions. Bio-inspired computing along with Artificial Intelligence enhances speed, dependability, and tractability. Also, bio-inspired computations allow adaptive systems to select suitable drives based on the current challenges.


Moreover, bio-inspired intelligent computing methods with working mechanisms deal with complex hard core problems by recognizing patterns improving adaptive systems' sensitivity and robustness. Bio-inspired computing in self-adaptive systems adds a feedback mechanism that monitors the system's state by adapting to internal and external changes to meet the goals. It also improves service-based system feedback by dynamically selecting services to keep the failure rate below a predefined threshold while minimizing cost. Besides, bio-inspired systems, including intelligent algorithms, change the adaptive systems into automatic, dynamic, and self-adaptive ones. Bio-inspired computing helps organize data in complex systems and enables extracting meaningful information by analyzing critical data with intelligent algorithms. Further, Bio-inspired computation, networking, and computation focus on efficient computing techniques for autonomous adaptive organizations and systems to solve real-world problems.

 

However, Due to the very complexity of computations in adaptive systems is difficult with limited time and expensiveness, apart from bio-inspired computing producing the best solutions even in hyper dimensional space. As a result, intelligent bio-inspired approaches are required to identify problems in a distributed environment. This special issue welcomes submissions of articles in all areas of adaptive and intelligence distributed systems ranging from concepts, theoretical developments, innovative technologies, and applications. The primary focus will be on the practical and real-world application of bio-inspired algorithms, techniques, and methods for exact systems and domains.

 

Possible Topics Include, but are not Limited to the Following:

 

• Reinventing robust adaptive system using bio-inspired algorithms.

• Potential bio-inspired methods and techniques for reducing complexity in adaptive systems.

• Modelling complex systems using bio-inspired mechanisms.

• Smart manufacturing system with advanced bio-inspired algorithms.

• Quantitative bio-inspired algorithms for enhancing complex engineering-based adaptive systems.

• A bio-inspired tool for intelligent and complex adaptive systems.

• Integrating machine learning and bio-inspired algorithms for complex engineering applications.

• Application of innovative bio-inspired techniques for optimizing complex problems.

• Identifying anomalies in the complex system using bio inspired mechanism.

• Analyzing -intricate patterns in adaptive systems with intelligent AI systems.


Keywords
Evolutionary Computation
Complex Adaptive Systems
Real-World Applications
Innovative Technologies
Distributed Systems
Intelligent Computing