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 2019
  • - Article ID 9325364
  • - Editorial

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

Jing Na | Zhile Yang | ... | Yimin Zhou
  • Special Issue
  • - Volume 2019
  • - Article ID 7125037
  • - Research Article

Efficient Conical Area Differential Evolution with Biased Decomposition and Dual Populations for Constrained Optimization

Weiqin Ying | Bin Wu | ... | Zhenyu Wang
  • Special Issue
  • - Volume 2019
  • - Article ID 4182148
  • - Research Article

Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation

Lin Sun | Suisui Chen | ... | Yun Tian
  • Special Issue
  • - Volume 2019
  • - Article ID 8759873
  • - Research Article

Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network

Shengyou Xu | Xin Yang | ... | Wei Lai
  • Special Issue
  • - Volume 2019
  • - Article ID 5379301
  • - Research Article

A Population-Based Optimization Method Using Newton Fractal

Soyeong Jeong | Pilwon Kim
  • Special Issue
  • - Volume 2019
  • - Article ID 2470376
  • - Research Article

Research on Pressurizer Pressure Control Based on Adaptive Prediction Algorithm

Hong Qian | Yuan Yuan | ... | Ting Yang
  • Special Issue
  • - Volume 2019
  • - Article ID 6320186
  • - Research Article

Research on UUV Obstacle Avoiding Method Based on Recurrent Neural Networks

Changjian Lin | Hongjian Wang | ... | Chengfeng Li
  • Special Issue
  • - Volume 2019
  • - Article ID 4727168
  • - Research Article

An External Archive-Based Constrained State Transition Algorithm for Optimal Power Dispatch

Xiaojun Zhou | Jianpeng Long | ... | Guanbo Jia
  • Special Issue
  • - Volume 2018
  • - Article ID 1450353
  • - Research Article

The Fractional Kalman Filter-Based Asynchronous Multirate Sensor Information Fusion

Guangyue Xue | Yubin Xu | ... | Wei Zhao
  • Special Issue
  • - Volume 2018
  • - Article ID 4057983
  • - Research Article

Optimization of Combustion Characteristics of Blended Coals Based on TOPSIS Method

Yingchun Liu | Hao Zhang | ... | Shuping Yang
Complexity
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Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.300
Impact Factor2.833
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