International Conference On Intelligent Computing
Wuhan,China August 15-18, 2018

ICIC 2018 Special Session

2018 International Conference on Intelligent Computing
August 15-18 ,2018
Wuhan,China
(http://ic-ic.tongji.edu.cn/2018/index.htm)

The ICIC 2018 Program Committee is inviting proposals for special sessions to be held during the conference (http://ic-ic.tongji.edu.cn/2018/index.htm), taking place on August 15-18 2018, in Wuhan,China.

Each special session proposal should be well motivated and should consist of 8 to 12 papers. Each paper must have the title, authors with e-mails/web sites, and as detailed an abstract as possible. The special session organizer(s) contact information should also be included. All special session organizers must obtain firm commitments from their special session presenters and authors to submit papers in a timely fashion (if the special session is accepted) and, particularly, present them at the ICIC 2018. Each special session organizer will be session chair for their own special sessions at ICIC 2018 accordingly. All planned papers for special sessions will undergo the same review process as the ones in regular sessions. All accepted papers for special sessions will also be published by Springer's Lecture Notes in Computer Sciences (LNCS)/ Lecture Notes in Artificial Intelligence (LNAI)/ Lecture Notes in Bioinformatics (LNBI).

All the authors for each special session must follow the guidelines in CALL FOR PAPERS to prepare your submitted papers.

Proposals for special sessions should be submitted in ELECTRONIC FORMAT to Special Session Chair:

Ling Wang,
Tsinghua University, China
wangling@tsinghua.edu.cn


1. Special Session on Evolutionary Optimization for Scheduling (Bo Liu, et. al, China)

Organizers:
Bo Liu
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Email:bliu@amss.ac.cn

Bin Qian
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Email: bin.qian@vip.163.com

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email:wangling@mail.tsinghua.edu.cn

Scope:
This special session invites papers discussing recent advances in the development and application of Evolutionary Optimization for Scheduling. Scheduling where a given set of jobs must be processed subject to various resource constraints, is a subject of substantial and well-developed research issue in communities of operations research and evolutionary optimization. The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both academician and practitioners working on Evolutionary Optimization for Scheduling to discuss new and existing issues in this area. We encourage papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following:

  • Novel evolutionary optimization approaches for scheduling
  • Many-objective optimization for scheduling
  • Hybrid approaches for scheduling
  • Interactive evolutionary scheduling optimization
  • Real-world scheduling applications based on evolutionary optimization

2. Special Session on Heuristic Optimization Algorithms for Real-world Applications (Wenyin Gong, et. al, China)

Organizers:
Wenyin Gong
School of Computer Science, China University of Geosciences, Wuhan, 430074, China
Email:wygong@cug.edu.cn

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email:wangling@mail.tsinghua.edu.cn/a>

Rui Wang
College of Systems Engineering, National University of Defense Technology, China
Email:ruiwangnudt@gmail.com

Scope:
Many real-world optimization problems are complex and very difficult to solve and they present a great challenge for the research communities. This is due to large and heavily constrained search spaces which make their modelling (let alone solving) a very complex task. Although they have received a significant amount of attention from various research communities, there is still a quest for an efficient and effective algorithm that is able to produce very good results within acceptable amount of time and can adapt itself to the solution landscape changes. Heuristic optimization algorithms, such as differential evolution, particle swarm optimization, ant colony optimization, and artificial bee colony, have proven to be effective solution methods for various optimization problems. The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both academician and practitioners on heuristic optimization algorithms to develop an efficient and effective algorithm that is able to support the decision maker and can produce very good results for the real-word problems in diverse fields. We encourage papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following:

  • Inversion in Geophysics
  • Energy systems
  • Computational fluid dynamics
  • Reservoir optimization in oil-fields
  • Weather prediction
  • Evolutionary robotics
  • Online control
  • Structural optimization
  • Medical imaging
  • Space applications
  • Molecular dynamics
  • Financial markets
  • Engineering design
  • Rapid prototyping
  • Manufacturing sciences
  • Drug design and pharmaceuticals
  • Vehicle routing
  • Micro electro-mechanical systems

3. Special Session on Evolutionary Multi-Objective Optimization and Its Applications (Rui Wang, et. al, China)

Organizers:
Rui Wang
College of Systems Engineering, National University of Defense Technology, China
Email:ruiwangnudt@gmail.com

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email:wangling@mail.tsinghua.edu.cn

Wenying Gong
School of Computer Science, China University of Geosciences, Wuhan, 430074, China
Email:wygong@cug.edu.cn

Lining Xing
College of Systems Engineering, National University of Defense Technology, China
Email:xingliningnudt@gmail.com

Scope:
This special session invites papers discussing recent advances in the development and application of biologically-inspired multi-objective optimization algorithms. Many problems from science and industry have several (and normally conflicting) objectives that have to be optimized at the same time. Such problems are called multi-objective optimization problems and have been subject of research in the past two decades. One of the reasons why evolutionary algorithms are so suitable for multi-objective optimization is because they can generate a whole set of solutions (the Pareto-optimal solutions) in a single run rather than requiring an iterative one-solution-at-a-time process as followed in traditional mathematical programming techniques.
The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both experts and new-comers working on Evolutionary Multi-objective Optimization (EMO) to discuss new and existing issues in this area.
We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are interested in application papers discussing the power and applicability of these novel methods to real-world problems in different areas in science and industry. You are invited to submit papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following:

  • Many-objective optimization
  • Theoretical aspects of EMO algorithms
  • Real-world applications of EMO algorithms
  • Test and benchmark problems for EMO algorithms
  • New EMO techniques including those using meta-heuristics such as artificial immune systems, particle swarm optimization, differential evolution, cultural algorithms, etc.
  • Multi-objectivization and visualization techniques
  • Handling practicalities, such as uncertainty, noise, constraints, dynamically changing problems, bi-level problems, mixed-integer problems, computationally expensive problems, fixed budget of evaluations, etc.
  • Performance measures for EMO algorithms
  • Techniques to keep diversity in the population
  • Comparative studies of EMO algorithms
  • Memetic and metaheuristics based EMO algorithms
  • Hybrid approaches combining, for example, EMO algorithms with mathematical programming techniques and exact methods
  • Parallel EMO approaches
  • Adaptation, learning, and anticipation
  • Evolutionary multi-objective combinatorial optimization, EMO control problems, EMO inverse problems, EMO data mining, EMO machine learning
  • Interactive Multi-objective Optimization

4. Special Session on Swarm Evolutionary algorithms for Scheduling and Combinatorial Optimization (Junqing Li, et. al, China)

Organizers:
Junqing Li
School of information science and engineering, Shandong Normal University, China
Email: lijunqing.cn@gmail.com

Hongyan Sang
School of Computer Science, Liaocheng University, China
Email: sanghongyan@lcu-cs.com

Kaizhou Gao
Nanyang Technological University
Email: kgao001@e.ntu.edu.sg

Yu-Yan Han
School of Computer Science, Liaocheng University, China
Email: hanyuyan@lcu-cs.com

Scope:
Swarm evolutionary algorithms and their applications in Scheduling and Combinatorial Optimization are active research areas in Artificial Intelligence, automation and Operations Research due to its applicability and interesting computational aspects. Evolutionary algorithms are suitable for scheduling and combinatorial optimization problems since they are highly flexible in terms of handling constraints, dynamic changes and multiple conflicting objectives.

This special session invites papers discussing recent advances in the development and application of biologically-inspired swarm optimization algorithms focus on solving scheduling and combinatorial optimization problems.

The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both experts and new-comers working on Swarm evolutionary algorithms for scheduling and combinatorial optimization to discuss new and existing issues in this area

This special session focuses on both theoretical and practical aspects of Swarm evolutionary algorithms and their applications in Scheduling and Combinatorial Optimization. Examples of swarm evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimization, particle swarm optimization, differential evolution, artificial bee colony, harmony search, chemical reaction optimization, teaching-and-learning-based optimization, pigeon-inspired optimization, water cycle algorithm, Jaya, evolutionary based hyper-heuristics, memetic algorithms and so on.

  • Production scheduling
  • Vehicle routing
  • Transport scheduling
  • Grid/cloud scheduling
  • Project scheduling
  • Space allocation
  • task assignment
  • traffic light scheduling
  • Multi-objective scheduling
  • Multiple interdependent decisions
  • Automated heuristic design
  • Building optimization
  • Energy saving optimization
  • Big data based scheduling optimization
  • Scheduling optimization in Cloud computing environment
  • Supply chain optimization
  • Parallel optimization algorithms
  • Knowledge-based evolutionary algorithms
  • Theoretical aspects of swarm evolutionary algorithms
  • New real-world and innovative applications

5. Special Session on Swarm Intelligence and Applications in Combinatorial Optimization (Gai-Ge Wang, et. al, China)

Organizers:
Gai-Ge Wang
Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China
Email: gaigewang@gmail.com

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email: wangling@mail.tsinghua.edu.cn

Yongquan Zhou
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
Email: yongquanzhou@126.com

Lining Xing
College of Systems Engineering, National University of Defense Technology, China
Email: xinglining@gmail.com

Scope:
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. In SI, an individual has a simple structure and its function is single. However, such systems composed by many individuals show the phenomenon of emergence and can address several difficult real world problems that are impossible to be solved by only an individual. During the recent decades, SI methods have been successfully applied to cope with complex and time-consuming problems that are hard to be solved by traditional mathematical methods. Therefore, SI is indeed a topic of interest amongst researchers in various fields of science and engineering. Some popular SI paradigms, including ant colony optimization, and particle swarm optimization, have been successfully applied to handle various practical engineering problems.

Combinatorial optimization is a subset of mathematical optimization that is related to operational research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, mathematics, auction theory, and software engineering. Many real world problems can be modeled and solved as combinatorial optimization problems. This is an active research area, where new formulations, algorithms, practical applications, and theoretical results are often proposed and published. Current challenges in the field involve modeling of hard problems, development of exact methods, design and experimental evaluation of approximate and hybrid methods, among others.

We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are interested in application papers discussing the power and applicability of these novel methods to combinatorial problems in different areas in science and industry. You are invited to submit papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following

  • Swarm Intelligence Algorithms
  • Improvements of traditional SI methods (e.g., ant colony optimization and particle swarm optimization)
  • Recent development of SI methods (e.g., monarch butterfly optimization, earthworm optimization algorithm, elephant herding optimization, moth search algorithm, bird swarm algorithm, chicken swarm optimization, fireworks algorithm, and brain storm optimization)
  • Theoretical study on SI algorithms using various techniques (e.g., Markov chain, dynamic system, complex system/networks, and Martingale)
  • Applications in Combinatorial Optimization
  • Scheduling (e.g., vehicle rescheduling, nurse scheduling problem, flow shop scheduling, and fuzzy scheduling)
  • Traveling salesman problem (e.g., symmetric traveling salesman problem, asymmetric traveling salesman problem, fuzzy traveling salesman problem, and other real world problems that can be converted to traveling salesman problem)
  • Knapsack problem (e.g., 0/1 knapsack problem, multi-objective knapsack problem, multi-dimensional knapsack problem, multiple knapsack problem, and quadratic knapsack problem)
  • Others (e.g., constraint satisfaction problem, set cover problem, task assignment problem, and portfolio optimization)