Accepted Special Sessions for ICSI 2018

 

 

Special Session 1: Self-adaptation Techniques in Evolutionary Computation

Qinqin Fan, Shanghai Maritime University, Shanghai, China
Weian Guo, Tongji University, Shanghai, China
Wei Du, East China University of Science and Technology, Shanghai, China
Xu Chen, Jiangsu University, Zhenjiang, China

Overview
Self-adaptation is one of the most vital roles for living things to adapt a changing environment in the natural world. Similarly, various optimization approaches always have to face different optimization environments, such as dynamic optimization, constrained optimization, multi/many-objective optimization, mixed integer optimization, and large-scale optimization. To address different types of problems effectively and efficiently, a large number of self-adaptation methods have been developed to improve the performances of algorithms in the field of optimization. However, there are still many open issues and intriguing challenges in the design of self-adaptation approaches.

Topics of interest
The aim of this special session is to highlight the recent developments in self-adaptation techniques used in evolutionary computation (EC). We invite authors to submit original and high-quality works on this topic including but not limited to:

  1. Novel self-adaptation approaches for parameter/search operator/construction of EC
  2. Design of self-adaptation framework in EC
  3. Self-adaptation methods using reinforcement learning/transfer learning in EC
  4. Machine learning assisted self-adaptation methods in EC
  5. Theoretical analysis for self-adaptation mechanism
  6. Real-world applications

Submission
Please follow the ICSI 2018 instruction for authors and submit your paper via the ICSI 2018 online submission system. Please specify that your paper is for the Special Session on Self-adaptation Techniques in Evolutionary Computation.

Organizers:
Qinqin Fan, Shanghai Maritime University, Shanghai, China, [email protected]
Weian Guo, Tongji University, Shanghai, China, [email protected]
Wei Du, East China University of Science and Technology, Shanghai, China, [email protected]
Xu Chen, Jiangsu University, Zhenjiang, China, [email protected]

Short Biography of the organizers:
Qinqin FAN received the Bachelor’s degree in Automation from Wuhan Institute of Technology, the Master’s degree from East China University of Science and Technology, the Ph.D. degree from East China University of Science and Technology, in 2007, 2011, and 2015, respectively. He is currently a lecturer of logistics research center at the Shanghai Maritime University since 2015. He has published over 20 articles in journals. His current research interests are in the area of multi-objective optimization, constrained optimization, hyper-heuristics, differential evolution, particle swarm optimization and their real-world applications.

Weian GUO received Ph,D of Control Theory and Engineering from Tongji University, Shanghai, China in 2014. From 2011 to 2013, he was invited as a visit scholar to carry on his research in Social Robotics Lab, National University of Singapore. Currently, he works as an associate professor in Sino-German College of Applied Sciences, Tongji University. His interests include computational intelligence, heuristic algorithms and the applications.

Wei DU received the B.S. and the M.S. degrees in electrical engineering from Donghua University, Shanghai, China, in 2009 and 2012, respectively, and the Ph.D. degree from The Hong Kong Polytechnic University, Hong Kong in 2016. He is currently a lecturer at East China University of Science and Technology, Shanghai, China. His current research interests include evolutionary computation, especially differential evolution, evolutionary multi-objective optimization, robust evolutionary multi-objective optimization, and their applications.



Special Session 2: Brain Storm Optimization Algorithms

Shi Cheng, Shaanxi Normal University, Xi’an, China, [email protected]
Yifei Sun, Shaanxi Normal University, Xi’an, China, [email protected]
Junfeng Chen, Hohai University, Changzhou, China, [email protected]
Yinan Guo, China University of Mining and Technology, Xuzhou, China, [email protected]

Overview

The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence algorithm, which is based on the collective behaviour of human being, that is, the brainstorming process. There are two major operations involved in BSO, i.e., convergent operation and divergent operation. A ``good enough'' optimum could be obtained through recursive solution divergence and convergence in the search space. The designed optimization algorithm will naturally have the capability of both convergence and divergence.
BSO possess two kinds of functionalities: capability learning and capacity developing. The divergent operation corresponds to the capability learning while the convergent operation corresponds to capacity developing. The capacity developing focuses on moving the algorithm's search to the area(s) where higher potential solutions may exist while the capability learning focuses on its actual search towards new solution(s) from the current solution for single point based optimization algorithms and from the current population of solutions for population-based swarm intelligence algorithms. The capability learning and capacity developing recycle to move individuals towards better and better solutions. The BSO algorithm, therefore, can also be called as a developmental brain storm optimization algorithm.
The capacity developing is a top-level learning or macro-level learning methodology. The capacity developing describes the learning ability of an algorithm to adaptively change its parameters, structures, and/or its learning potential according to the search states of the problem to be solved. In other words, the capacity developing is the search potential possessed by an algorithm. The capability learning is a bottom-level learning or micro-level learning. The capability learning describes the ability for an algorithm to find better solution(s) from current solution(s) with the learning capacity it possesses.
The BSO algorithm can also be seen as a combination of swarm intelligence and data mining techniques. Every individual in the brain storm optimization algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Topics of Interest

This special session aims at presenting the latest developments of BSO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special session.
Potential topics include, but are not limited to:

  • Theoretical aspects of BSO algorithms;
  • Analysis and control of BSO parameters;
  • Parallelized and distributed realizations of BSO algorithms;
  • BSO for multiple/many objective optimization;
  • BSO for constrained optimization;
  • BSO for discrete optimization;
  • BSO for large-scale optimization;
  • BSO algorithm with data mining techniques;
  • BSO in uncertain environments;
  • BSO for real-world applications.

Submission

Please follow the ICSI 2018 instruction for authors and submit your paper via the ICSI 2018 online submission system. Please specify that your paper is for the Special Session on Brain Storm Optimization Algorithms.

Organisers

Shi Cheng, Shaanxi Normal University, Xi’an, China, [email protected]
Yifei Sun, Shaanxi Normal University, Xi’an, China, [email protected]
Junfeng Chen, Hohai University, Changzhou, China, [email protected]
Yinan Guo, China University of Mining and Technology, Xuzhou, China, [email protected]

 


 

Special Session 3: The Applications of Evolutionary Computation in Real Time Task Allocation of Complex Embedded System

Zhang Tao, Tianjin University, Tianjin, China, [email protected]

Qu Tianshu, Peking University, Beijing, China, [email protected]

Wang Xiaochen, Wuhan University, Wuhan, China, [email protected]

Introduction

The complex embedded system, which is represented by heterogeneous multi-core processors, has features of high processing performance and wide application areas. With the development of artificial intelligence, big data and other new technologies, the requirements for the performance of the embedded system are getting higher and higher, the complex embedded system is becoming the main platform to implement these technologies. As one of the keys in the development of embedded system, task allocation has a direct impact on the ability of the embedded system to deal with complex tasks. And completing the task allocation of the complex embedded system in real time is the primary requirement for task allocation algorithm. Using evolutionary computation for task allocation is one of the most important applications of evolutionary computation. But when the problem is complex, evolutionary computation usually take a long time to get the solution. Therefore, designing the efficient evolutionary computation method and applying it to real time task allocation of complex embedded system is a very meaningful work.
This special session researches on applying evolutionary computation to task allocation of complex embedded system, which is an import application of evolutionary algorithm and not included in the regular sessions. More importantly, the special session focuses on real time task allocation method which will study on the efficient evolutionary computation method and the simplify methods of problem models. These researches will provide a complementary flavor to the regular sessions.

Short biography of the organizers:

1Zhang Tao received the M.S. degree in School of Electronic Information Engineering from Tianjin University, Tianjin, China, in 2001, the Ph.D. degree from the same University in 2004. He is currently an associate professor at the School of Electrical and Information Engineering, Tianjin University and the  Director of Tianjin University & Texas Instruments DSP Joint Lab. His current interests include design of complex embedded system, application for swarm intelligence algorithm and Multimedia processing.

2Qu Tianshu received the Ph.D degree in Jinlin University, Jilin, China, in 2002. He is currently an associate professor at School of Electronics Engineering and Computer Science, Peking University, China. His current interests include theory and method for high-fidelity image, complex system design and multimodal interaction technology.


3Wang Xiaochen received M.S. degree in Wuhan University, Hubei, China, in 2003 and the Ph.D degree from the same university in 2011. He is currently the lecturer at School of Computer Science, Wuhan University, China. His current research interests include mobile speech and audio coding, speech intelligibility enhancement, embedded system design and artificial intelligence.

 

List of 5 – 6 contributed papers:

  1. An Efficient Task Allocation Method Based on A Simplified Model and Evolutionary Computation
  2. Corresponding Author: Qu Tianshu
    Email: [email protected]

  3. Hardware/Software Partitioning Based on An Improved Firework Algorithm
  4. Corresponding Author: Zhang Tao
    Email: [email protected]

  5. An Fast Task Assignment Algorithm Based on GPU Acceleration
  6. Corresponding Author: Zhang Tao
    Email: [email protected]

  7. Task Allocation of Multicore Processors with An Efficient Hybrid Evolutionary Algorithm
  8. Corresponding Author: Wang Xiaochen
    Email: [email protected]

  9. Using An Improved Shuffled Frog Leaping Algorithm for Task Allocation and Scheduling of Multicore Processors
  10. Corresponding Author: Zhao Xin
    Email: [email protected]

 


 

Special Session 4: Quantum-behaved Particle Swarm Optimization Algorithm and Its Applications


Wei Fang, Jiangnan University, Wuxi, China, [email protected]
Yangyang Li, Xidian University, Xi’an, China, [email protected]
Yi Mei, Victoria University of Wellington, New Zealand, [email protected]
Shi Cheng, Shaanxi Normal University, Xi’an, China, [email protected]


Overview
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm and it has attracted a large number of widespread researchers. As a branch of PSO, a probabilistic PSO algorithm, which is quantum-behaved PSO (QPSO), was proposed on the quantum mechanics and trajectory analysis of PSO. QPSO shines for its simplicity, easy implementation, and fine search ability. The QPSO algorithm has been shown to offer good performance in solving a wide range of continuous optimization problems and many efficient strategies have been proposed to improve the algorithm. The strategies of improvements range from parameter selection to control swarm diversity, to cooperative methods, to using probability distribution function, to novel search methods, and to hybrid methods with other techniques. The application topics of QPSO include clustering and classification, control, electronics and electromagnetics, biomedical, graphics and image processing, signal processing, power systems, neural network, fuzzy, modeling, antennas, combinatorial optimization, etc. For more details of QPSO algorithm, please refer to [1][2][3].
[1] Wei Fang, Jun Sun, Huanhuan Chen, Xiaojun Wu. A Decentralized Quantum-inspired Particle Swarm Optimization Algorithm with Cellular Structured Population [J]. Information Sciences. 2016, 330(10), 19-48.
[2] Jun Sun, Wei Fang, Xiaojun Wu, Zhenping Xie, Wenbo Xu. Quantum-behaved particle swarm optimization: analysis of the individual particle’s behavior and parameter selection[J]. Evolutionary Computation(MIT Press). 2012,20(3): 349–393.
[3] Wei Fang, Jun Sun, Yanrui Ding, Xiaojun Wu, Wenbo Xu. A review of quantum-behaved particle swarm optimization[J]. IETE Technical Review, 2010, 27(4): 336-348.

 

Topics of interest 
The aim of this special session is to highlight the recent developments in QPSO algorithm. We invite authors to submit original and high-quality works on this topic including but not limited to:

  1. Novel variants of QPSO algorithm
  2. QPSO algorithm for different types of problems, including multi-modal problem, large scale optimization problem, multi-objective problem, etc.
  3. QPSO algorithm for real-world applications
  4. Theoretical analysis for QPSO algorithm

Submission
Please follow the ICSI 2018 instruction for authors and submit your paper via the ICSI 2018 online submission system. Please specify that your paper is for the Special Session on Quantum-behaved Particle Swarm Optimization Algorithm and Its Applications.


Organizers:
Wei Fang, Jiangnan University, Wuxi, China, [email protected]
Yangyang Li, Xidian University, Xi’an, China, [email protected]
Yi Mei, Victoria University of Wellington, New Zealand, [email protected]
Shi Cheng, Shaanxi Normal University, Xi’an, China, [email protected]


Short Biography of the organizers:

Wei Fang received the Ph.D. degree in information technology and engineering of light industry from Jiangnan University, Wuxi, China, in 2008. He is an Associate Professor of computer science at Jiangnan University. He was a Visiting Scholar at the University of Birmingham, Birmingham, U.K during year 2013-2014. He has published more than 20 scientific papers in journals and international conferences. His current research interests involve the design and application of evolutionary algorithms, particularly in swarm intelligence.

Yangyang Li received the B.S. and M.S. degrees in Computer Science and Technology from Xidian University, Xi’an, China, in 2001 and 2004 respectively, and the Ph.D. degree in Pattern Recognition and Intelligent System from Xidian University, Xi’an, China, in 2007.
She is currently a professor in the school of Artificial Intelligence at Xidian University. Her current research interests include quantum-inspired evolutionary computation, artificial immune systems, and data mining.

Yi Mei is a Lecturer at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. He received his BSc and PhD degrees from University of Science and Technology of China in 2005 and 2010, respectively. His research interests include evolutionary computation in scheduling, routing and combinatorial optimisation, as well as evolutionary machine learning, genetic programming, feature selection and dimensional reduction.

 


 

Special Session 5: Evolutionary Computation for Power and Energy Systems

Zhi-Le Yang, Shenzhen Institute of Advanced Technology, CAS, [email protected]

Qun Niu, Shanghai University, China, [email protected]

Haiping Ma, Shaoxing University, China, [email protected]

Bo-Yang Qu, Zhongyuan University of Technology, China, [email protected]


Motivation and Scope

Shaping low carbon energy future calls for technical breakthroughs in clean and sustainable power and energy systems. Numerous non-convex complex optimisation problems have been formulated and solved to effectively save the fossil fuel cost and relief energy waste. Evolutionary computation is promising to provide powerful optimisation tools for intelligently and efficiently solving problems such as power and sustainable energy systems scheduling to reduce carbon consumptions.
This special session intends to bring together the state-of-the-art advances of evolutionary optimisation approaches for solving emerging problems in complex modern power and sustainable energy systems. The submissions are encouraged to be focus on smart grid scheduling, power system integrations of new participants such as renewable generations and plug-in electric vehicles, thermodynamic optimisation for heat exchanger design and Organic Rankine Cycle, parameters identification for photovoltaic models and PEM fuel cells and other energy optimisation topics.

A brief list of potential submission topic is shown below:

  • Single or multiple objectives techniques for power and energy system applications
  • Unit commitment, economic dispatch and optimal power flow
  • Optimal smart grid scheduling and integration with renewable energy generations
  • Efficient power-train management for hybrid electric vehicles
  • Charging and discharging strategies for energy storage battery systems
  • Parameters identification for photovoltaic models and PEM fuel cells
  • Thermodynamic optimisation for heat exchanger design and Organic Rankine Cycle
  • Energy reduction strategies for energy intensive manufacturing processes

 

Organizers:


Zhi-Le Yang
Shenzhen Institute of Advanced Technology
Chinese Academy of Sciences,
Shenzhen, 518055, China
[email protected]

Qun Niu
School of Mechatronics and Automation,
Shanghai University,
Shanghai, China
[email protected]

Haiping Ma
College of Mathematics, Physics and Information,
Shaoxing University,
Shaoxing, China
[email protected]

Bo-Yang Qu
School of Electrical and Information Engineering,
Zhongyuan University of Technology
Zhengzhou, 450007, China
[email protected]

 

Short Biography of organizers:

Dr. Zhi-Le Yang obtained BSc and MSc degrees both at Shanghai University, China, and received Ph.D. degree at Queen’s University Belfast, UK. He is currently an assistant professor at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China. His research interests focus on computational intelligence especially evolutionary computation methods and their applications on smart grid, renewable energy, electric vehicles and various energy systems. He is the author or co-author of more than 40 articles in peer reviewed international journals and conferences. He was the founding chair of IEEE QUB student branch and an active member of IEEE PES, CIS and SMC societies. He has been the secretary general for several international conferences and an active reviewer for over 20 peer reviewed international journals.

Dr. Qun Niu received the B.Sc. degree in automation and the Ph.D. degree in control theory and control engineering from the East China University of Science and Technology, Shanghai, China, in 2002 and 2007, respectively. She is currently an Associate Professor with the Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai. Her main research interests include intelligent computing with applications to power systems and renewable energy, scheduling and optimization, manufacturing processes.

Dr. Hai-ping Ma received the B. S. degree from Shaoxing University, Shaoxing, China, the M. S. degree from the Taiyuan University of Technology, Taiyuan, China, and the Ph. D. degree from Shanghai University, Shanghai, China, in 2004, 2007, and 2014, respectively, all in control theory and control engineering. He is currently an Associate Professor with the College of Mathematics, Physics and Information, Shaoxing University. From 2015 to 2016, he was a Postdoctoral research fellow with the Group of Networked Sensing and Control, Zhejiang University, Hangzhou, China. From 2016 to 2017, he was a visiting scholar with the school of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast, UK. In 2015, he received the Outstanding Ph. D. Dissertation Award, Chinese Association of System Simulation, China. He has published over 30 research papers on evolutionary algorithms and applications. His current research interests include evolutionary computation, information fusion, and intelligent control. He is the author of the textbook Evolutionary Computation with Biogeography-based Optimization (Wiley-ISTE, 2017).

Dr. Bo-Yang Qu received the B.E. degree and Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He is an Associate Professor in the School of Electric and Information Engineering, Zhongyuan University of Technology, China. His research interests include machine learning, neural network, genetic and evolutionary algorithms, swarm intelligence, and multi-objective optimization.  He has published over 100 research papers on evolutionary algorithms and applications.


 

Special Session 6: Swarm Intelligence Algorithms, Simulation, Theories and Applications

Ben Niu,   Shenzhen University, China,  [email protected]
Hong Wang, Shenzhen University, China,  [email protected]
Liang Jing, Zhengzhou University, China, [email protected]
Kaizhou Gao, Nanyang Technological University, Singapore, [email protected]


Scope:


Swarm Intelligence (SI) refers to the problem-solving capability by taking inspiration from the collective activities of social organisms like the birds, fishes, ants, bees, bacteria, and human beings. The basic operators, the life-cycle principles, the interactions between the simple-information-processing colonies, and the unique exploration and exploitation strategies can widen the insights of humans to manage the complex systems from distinct aspects. The typical SI algorithms include Particle Swarm Optimization, Ant Colony Optimization, Bacterial Foraging Optimization, and Bee Colony Optimization, etc. The applications of those optimization algorithms are fairly vast such as job scheduling, data mining, design optimization, and pattern recognition.

The special session is to collect a series of latest advantages and contributions in theories, technologies, and simulations. Applications of those swarm intelligence algorithms are all welcome. Research areas relevant to the special issue include, but are not limited to, the following topics:

  1. Particle swarm optimization
  2. Bacterial foraging optimization
  3. Ant colony optimization
  4. Bee colony optimization
  5. Artificial fish search algorithm
  6. Harmony search algorithm
  7. Jaya algorithm
  8. Water cycle algorithm
  9. Other swarm and evolutionary based algorithms

Applications of the above algorithms include but not limited to

  1. Operations research
  2. Decision making
  3. Management optimization
  4. Information systems
  5. Power and energy systems
  6. Data mining
  7. Multi-objective optimization
  8. Pattern recognition
  9. Robotics
  10. Manufacturing system scheduling
  11. Intelligent Transportation and Traffic
  12. Maritime optimization and scheduling
  13. Other relating applications

Submission:


Please follow the ICSI 2018 instruction for authors and submit your paper via the ICSI 2018 online submission system. Please specify that your paper is for the Special Session on Swarm Intelligence Algorithms, Simulation, Theories and Applications.

Organizers:


Prof. Ben Niu,   Shenzhen University     [email protected]
Dr. Hong Wang,  Shenzhen University     [email protected]
Prof. Liang Jing,  Zhengzhou University    [email protected]
Dr. Kaizhou Gao,  Nanyang Technological University  [email protected]

 

Special Session 7: Multi-Objective Evolutionary AlgorithmApplications

Juan Zou, Xiangtan University, China. [email protected]
Nanjiang Dong, Xiangtan University, China. [email protected]
Yaru Hu, Xiangtan University, China.  [email protected]
Shengxiang Yang, De Montfort University, United Kingdom, and Xiangtan University,China. [email protected]

Overview
Multi-objective optimization problems (MOPs) are commonly seen in real-world applications, especially in the areas of engineering, biology, and economics. Such optimization problems are characterized by multiple objectives that conflict with each other. Due to the conflicting nature of the objectives, usually no single optimal solution exists; instead, a set of trade-off solutions, known as Pareto optimal solutions can be found for MOPs. Multi-objective evolutionary algorithm plays a significant role and has gained promising results in multi-objective optimization problems.


Topics of Interest
The special session aims at making the researchers from various countries together to summarize the latest research theory and the related application of the multi-objective evolutionary optimization, and explore the developed direction for the future research of multi-objective evolutionary optimization. Topics of interest include, but are not limited to:

  • Evolutionary Computation for Dynamic Multi-objective Optimization with Dynamic Preference
  • The real-world applications of the many-objective evolutionary algorithm
  • Many-objective evolutionary optimization
  • Preference-based many-objective evolutionary optimization
  • The balance of convergence and diversity
  • Benchmark problems and performance measures
  • Models of many-objective evolutionary optimization
  • Correlation analysis about decision variables and target variables
  • Adaptation, learning, and anticipation
  • Hybrid environment selection
  • Using fitness approximations
  • Searching for robust optimal solutions
  • Hybrid approaches
  • Application of multi-objective evolutionary optimization methods

Submission
Please follow the ICSI 2018 instruction for authors and submit your paper via the ICSI 2018 online submission system. Please specify that your paper is for the Special Session on Multi-Objective Evolutionary Algorithm.


Organizers:

Associate Prof. Juan Zou,
Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China.
Email: [email protected]

Master. Nanjiang Dong,
Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China.
Email: [email protected]

Master. Yaru Hu,
Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China.
Email: [email protected]

Prof. Shengxiang Yang,
School of Computer Science and Informatics, De Montfort University, United Kingdom.
Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China.
Email: [email protected]


 

Special Session 8: Biologically-inspired Swarm Intelligence for Robotics and Mechatronics

Chaomin Luo, University of Detroit Mercy, Michigan, USA, [email protected]

Objectives

Biologically-inspired swarm intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has  had  an immense impact on our economy and society, and this trend will continue with biologically inspired swarm intelligence techniques. Biologically-inspired intelligence, such as biologically-inspired neural networks (BINN), is about learning from nature, which can be applied to the real world robot and vehicle systems. Recently, the research and development of bio-inspired systems for robotic applications is increasingly expanding worldwide. Biologically-inspired algorithms contain emerging sub-topics such as bio-inspired neural network algorithms, brain-inspired neural networks, swam intelligence with BINN, ant colony optimization algorithms (ACO) with BINN, bee colony optimization algorithms (BCO), particle swarm optimization with BINN, immune systems with BINN, and biologically-inspired evolutionary optimization and algorithms, etc. Additionally, it is decomposed of computational aspects of bio-inspired systems such as machine vision, pattern recognition for robot and vehicle systems, motion control, motion planning, movement control, sensor-motor coordination, and learning in biological systems for robot and vehicle systems.
This special session seeks to highlight and present the growing interests in emerging research, development and applications in the dynamic and exciting areas of biologically-inspired algorithms for robot and vehicle systems (autonomous robots, unmanned underwater vehicles, and unmanned aerial vehicles).

Scope and Topics

Original research papers are solicited in related areas of biologically-inspired algorithms for robotics. Submissions to the Special Session should be focused on theoretical results or innovative applications of computational intelligence of biologically-inspired algorithms (such as BINN) for robot and vehicle systems.

Specific topics for the special session include but are not limited to:

  • Biologically-inspired neural networks for robotics
  • Deep neural networks and learning systems for robotics such as motion planning, navigation, mapping, localization, image processing, etc
  • Bio-inspired system on computer vision and image progressing for robotics
  • Human-like learning for robotics
  • Theory, design, and applications of neural networks and related learning systems for robotics and vehicles
  • Neuro-dynamics based models for robot and vehicle systems
  • Evolutionary optimization,  machine vision, pattern recognition for robot and vehicle systems
  • Brain-inspired neural networks for robotics
  • Swarm intelligence for robotics
  • Evolutionary neuro-computing for robot and vehicle systems
  • Bio-inspired system on machine learning, intelligent systems design for robotics
  • Cellular automata for robotics
  • Immune systems with BINN for robotics
  • Ant colony optimization algorithms (ACO) with BINN for robotics
  • Bee colony optimization algorithms (BCO) with BINN for robotics.

Organizers: 1

Session Chair:Dr. Chaomin Luo, PhD., Associate Professor, Department of Electrical and Computer Engineering, University of Detroit Mercy, Michigan 48221-3038, USA, [email protected]

Dr. Luo’s biograph:

Dr. Chaomin Luo received his Ph.D. in Department of Electrical and Computer Engineering at University of Waterloo, Canada in 2008, his M.Sc. in Engineering Systems and Computing at University of Guelph, Canada, and his B.Eng. degree in Electrical Engineering from Southeast University, Nanjing, China. He was a Research Associate in the Department of Electrical and Computer Engineering, at McMaster University in 2003. His extensive industry experience contains working as an Electronics Engineer, Hardware Designer and the Director of the Embedded Systems and Intelligent Instrument Lab in Canada, Singapore and China. He is currently an Associate Professor in Department of Electrical and Computer Engineering, at University of Detroit Mercy, Michigan, USA. His research interests include Intelligent System, Computational Intelligence, Robotics and Automation, Embedded Systems, and Electronic Design Automation of VLSI/FPGA Circuits.
He has shown his very strong leadership nationally and internationally on his research field. He was the General Co-Chair of the 1st IEEE International Workshop on Computational Intelligence in Smart Technologies (IEEE-CIST 2015), and Journal Special Issues Chair, IEEE 2016 International Conference on Smart Technologies (IEEE-SmarTech), Cleveland, OH, USA. He was the Publicity Chair in the 2011 IEEE International Conference on Automation and Logistics. He was on the Conference Committee in the 2012 International Conference on Information and Automation and International Symposium on Biomedical Engineering and also the Publicity Chair in the 2012 IEEE International Conference on Automation and Logistics. Also, he was Chair and Vice Chair of IEEE SEM - Computational Intelligence Chapter and is currently a Chair of IEEE SEM - Computational Intelligence Chapter and Chair of Education Committee of IEEE SEM. Dr. Luo serves as the Editorial Board Member of Journal of Industrial Electronics and Applications, and International Journal of Complex Systems – Computing, Sensing and Control, Associate Editor of International journal of Robotics and Automation, and Associate Editor of International Journal of Swarm Intelligence Research (IJSIR). He has organized and chaired several special sessions on topics of Intelligent Vehicle Systems and Bio-inspired Intelligence in IEEE reputed international conferences such as IEEE-IJCNN, IEEE-SSCI, etc. He has extensively published in reputed journal and conference proceedings, such as IEEE Transactions on Neural Networks, IEEE Transactions on SMC, IEEE Transactions on Cybernetics, IEEE-ICRA, and IEEE-IROS, etc. He was the Panelist in the Department of Defense, USA, 2015-2016, 2016-2017 NDSEG Fellowship program, and Panelist in 2017 NSF GRFP Panelist program.


A list of potential contributors

Mahmoud Abou-Nasr, PhD.
Ford Motor Company, USA, [email protected]

Qirong TANG Professor, Dr.-Ing.
Vice Dean of School of Mechanical Engineering,
Founding Director of Laboratory of Robotics and Multibody System (RMB), Tongji University, Shanghai 201804, China

Simon X. Yang, Ph.D.
Professor, Director, Advanced Robotics and Intelligent Systems (ARIS) Lab School of Engineering, University of Guelph
Guelph, Ontario, N1G 2W1, Canada Email: [email protected]

Yi Lu Murphey, Ph.D.
Professor and Associate Dean, IEEE Fellow Department of Electrical and Computer Engineering University of Michigan at Dearborn
USA
Email: [email protected]

Max Q.-H. Meng, Ph.D. Professor, Division Head IEEE Fellow
Fellow of Canadian Academy of Engineering Fellow of Hong Kong Institute of Engineers Department of Electronic Engineering
The Chinese University of Hong Kong Hong Kong, China
Email: [email protected], [email protected]

Xiang Cao, Ph.D. Lecturer
School of Physics and Electronic Electrical Engineering Huaiyin Normal University, China

Gene Eu Jan, Ph.D. Professor
Past Dean of College of Electrical and Information Engineering Department of Electrical and Computer Engineering
National Taipei University Taipei, Taiwan
Email: [email protected]

Zhuming Bi, Ph.D.
Professor of Mechanical Engineering Department of Civil and Mechanical Engineering
Indiana University–Purdue University Fort Wayne (IPFW) 2101 E. Coliseum Blvd., Fort Wayne, IN 46805-1499 USA
E-mail: [email protected]

Howard Li, Associate Professor, P.Eng, PhD. Department of Electrical and Computer Engineering University of New Brunswick
Fredericton, NB E3B 5A3 Canada Email: [email protected]

Wenbing Zhao, Professor, PhD.
Department of Electrical and Computer Engineering, Cleveland State University, USA
Emails: [email protected]

Yu Sun, PhD.
Division of Vehicle Control, Magna International USA Email: [email protected]

Prof. Kun Hua, Associate Professor, PhD. Department of Electrical and Computer Engineering Lawrence Technological University, USA
Email: [email protected]

Furao Shen, Associate Professor, PhD.
Robotic Intelligence and Neural Computing Laboratory Department of Computer Science and Technology Nanjing University
Email: Shen Furao <[email protected]>

Steven E. Muldoon, General Motors, Michigan, USA, [email protected]

Zijiang James Yang, Associate Professor, PhD. Western Michigan University,
USA; [email protected]

Xiangdong Che, Assistant Professor, PhD. Eastern Michigan University, USA; [email protected]

John Gao, PhD.
DENSO International America, Inc., USA, [email protected]

Qimi Jiang, PhD.
Comau Inc, North America, USA [email protected]

Ling Zhuang, Assistant Professor, PhD. Wayne State University, USA, [email protected]

Qiuming Gong, PhD.
Ford Motor Company, USA, [email protected]


 

ICSI Call for Special Session Proposals

ICSI 2018 technical program will include special sessions. Their aim is to provide a complementary flavor to the regular sessions and should include hot topics of interest to the swarm intelligence community that may also go beyond disciplines traditionally represented at ICSI.

Prospective organizers of special sessions should submit proposals indicating:

* Title of the session.
* Rationale of the need for the special session at ICSI.
The rationale should stress the novelty of the topic and/or its multidisciplinary flavor, and must explain how it is different from the subjects covered by the regular sessions.
* Short biography of the organizers.
* List of 5 – 6 contributed papers (including titles, authors, contact information of the corresponding author) (this can also be provided later when they become available).

Proposals are due on or before January 01, 2018 and should be sent via e-mail (in either pdf or plain ASCII text form) to the special sessions chair (Prof. Ben Niu ([email protected]) or Prof. Yinan Guo ([email protected])) and forward to ICSI 2018 secretariat at [email protected].

Proposals will be evaluated based on the timeliness of the topic, the qualifications of the organizers and the authors of the papers proposed in the session. In its decision, the committee will try to realize a balance of the topics across the technical areas represented in swarm intelligence.

Notification of acceptance will be sent to the organizers no later than January 1, 2018. Authors of papers included in approved special sessions should submit their manuscript on or before January 30, 2018. Manuscripts should conform to the formatting and electronic submission guidelines of regular ICSI papers (Springer’s LNCS format).

When they submit papers, there is a choice to indicate that their papers are special session papers which will also undergo peer review. It is the responsibility of the organizers to ensure that their special session meets the ICSI quality standards. If, at the end of the review process, less than four papers are accepted, the session will be canceled and the accepted papers will be moved to regular sessions.