Complexity

Bio-Inspired Learning and Adaptation for Optimization and Control of Complex Systems


Publishing date
01 Nov 2018
Status
Published
Submission deadline
13 Jul 2018

Lead Editor

1University of Bristol, Bristol, UK

2Queen’s University Belfast, Belfast, UK

3Indian Institute of Technology (BHU), Varanasi, India

4De Montfort University, Leicester, UK

5Nanyang Technological University, Singapore

6Chinese Academy of Sciences, Beijing, China


Bio-Inspired Learning and Adaptation for Optimization and Control of Complex Systems

Description

Learning and adaptation are playing important roles in solving numerous complex science and engineering problems, particularly including artificial intelligence, complex system analysis, control engineering, and many multidisciplinary topics. In this respect, some bio-inspired methods, such as reinforcement learning, coevolution learning, and chaos, genetic algorithms, cellular automata, and neural networks, provide essential tools to solve various optimization and control problems of complex systems (e.g., chaotic systems, multiagent systems, and distributed smart grid). This has also stimulated recently increasing research interests and developments on learning and adaptation with particular application to modeling, optimization, and control for complex systems with nonlinear dynamics. Investigating the fundamental properties of bio-inspired learning and adaptation methods (e.g., neural networks, genetic algorithms, and evolutionary game) and showcasing their applications in complex systems (e.g., chaotic systems, social systems, and multiagent systems) could not only promote better understanding of the underlying mechanisms of bio-inspired systems but also provide a possibility to explore their potential to solve complex system behavior analysis, modeling, and control.

This special issue aims at providing a specific opportunity to review the state of the art of this emerging and cross-disciplinary field of bio-inspired learning and adaptation with particular application to complex systems. Authors are invited to present new algorithms, frameworks, software architectures, experiments, and applications aimed at bringing new information about relevant theory and techniques of learning and adaptation. All original papers related to analysis, learning, and adaptation and their application for optimization and control of complex systems are welcome. In particular, we encourage authors to introduce new results for synthesizing learning and optimization into practical complex systems, for example, chaotic systems, smart grid, population systems, multiagent systems, social systems, UAVs, and human-robot interactions.

Potential topics include but are not limited to the following:

  • Neural network based learning and adaptation algorithms
  • Bio-inspired optimization algorithms including genetic algorithm and particle swarm optimization
  • Game theory via advanced adaptation and learning
  • Data-driven based optimization of complex systems
  • Online/offline policy iteration algorithm and reinforcement learning algorithms
  • Distributed learning and optimization methods
  • Learning based identification and observer design of complex systems
  • Learning based chaotic systems, social systems, and multiagent systems
  • Learning and adaptation approaches based on big data
  • Learning and adaptation approaches for power and energy engineering
  • Learning and adaptation approaches for unmanned vehicles and robotics

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 9765861
  • - Research Article

Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc-Wen Model

Xuehui Gao
  • Special Issue
  • - Volume 2018
  • - Article ID 7353171
  • - Research Article

Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise

Junhong Li | Xiao Li
  • Special Issue
  • - Volume 2018
  • - Article ID 9267054
  • - Research Article

Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization

Xingguang Peng | Yapei Wu
  • Special Issue
  • - Volume 2018
  • - Article ID 9598307
  • - Research Article

Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Jian Pan | Hao Ma | ... | Feng Ding
  • Special Issue
  • - Volume 2018
  • - Article ID 8420426
  • - Research Article

Instrumental Variable-Based OMP Identification Algorithm for Hammerstein Systems

Shuo Zhang | Dongqing Wang | Yaru Yan
  • Special Issue
  • - Volume 2018
  • - Article ID 4136972
  • - Research Article

Desired Compensation Adaptive Robust Control of an Active Vibration Isolation System

Bo Zhao | Weijia Shi | ... | Feng Li
  • Special Issue
  • - Volume 2018
  • - Article ID 2032461
  • - Research Article

A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

Rong Zhou | Yong Zhang | ... | Nurbol Luktarhan
  • Special Issue
  • - Volume 2018
  • - Article ID 7465391
  • - Research Article

Robust Control of Pressure for LNG Carrier Cargo Handling System via Mirror-Mapping Approach

Jinghua Cao | Xianku Zhang | ... | Xiang Zou
  • Special Issue
  • - Volume 2018
  • - Article ID 7289674
  • - Research Article

An Improved Particle Swarm Optimization with Biogeography-Based Learning Strategy for Economic Dispatch Problems

Xu Chen | Bin Xu | Wenli Du
  • Special Issue
  • - Volume 2018
  • - Article ID 8925838
  • - Research Article

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Shubo Wang | Haisheng Yu | ... | Na Wang
Complexity
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate11%
Submission to final decision120 days
Acceptance to publication21 days
CiteScore4.400
Journal Citation Indicator0.720
Impact Factor2.3
 Submit Check your manuscript for errors before submitting

Article of the Year Award: Impactful research contributions of 2022, as selected by our Chief Editors. Discover the winning articles.