Special Sessions

ICSI Call for Special Session Proposals

ICSI 2021 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 1, 2021 and should be sent via e-mail (in either pdf or plain ASCII text form) to the special sessions chair (to be announced) and forward to ICSI 2021 secretariat at icsi2021@iasei.org.

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, 2021. Authors of papers included in approved special sessions should submit their manuscript on or before January 30, 2021. 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.

 

Special Session 1: "Intelligent Computing and Swarm Intelligence in Management Science and Industrial Applications"

With the rapid development of computation power and artificial intelligence, swarm intelligence and intelligent computing have attracted both researchers and engineers in the field of management science, computer engineering, scheduling, bioinformatics, data mining, design optimization, etc. Inspired by the behavior in self-organized groups, such as ants, human beings, bacteria, bees, fireflies, cuckoo birds, fishes and weeds, some potential basic principles have been dug out to construct swarm intelligence algorithms. With the inherent self-organized and distributed characteristics, swarm intelligence algorithms have shown strong ability in solving nondeterministic polynomial-time hard (NP-hard) problems in management science and industrial applications.


Topics of Interest

Research areas relevant to the special issue include, but are not limited to, the following topics:

  • Particle Swarm Optimization
  • Bacterial Foraging Optimization
  • Genetic Algorithm
  • Hydrogical Cycle Optimization
  • Grey Wolf Optimizer
  • Bee Colony Optimization
  • Brain Storm Optimization Algorithm
  • Artificial Fish Search Algorithm
  • Harmony Search Algorithm
  • Multi-objective Optimization
  • Machine Learning
  • Intelligent Transportation and Traffic
  • Data Mining
  • Image Processing
  • Manufacturing System Scheduling
  • Pattern Recognition
  • Maritime Optimization and Scheduling
  • Operations Research
  • Decision Making
  • Management Optimization
  • Information Systems
  • Power and Energy Systems
  • Unmanned Vehicle
  • Robotics
  • Other Intelligent Computing and Swarm Intelligence Related Algorithms and Applications

    • Submission


    Please follow the ICSI 2021 instruction for authors and submit your paper via the ICSI 2021 online submission system. Please specify that your paper is for "SS1" -- the Special Session on Recent Advances in Swarm Intelligence and Intelligent Computing：Algorithms and Applications in your paper.

    Organizers


    Prof. Ben Niu Shenzhen University drniuben@gmail.com
    Dr. Tianwei Zhou Shenzhen University tianwei@szu.edu.cn
    Prof. Jiang Liang Zhengzhou University liangjing@zzu.edu.cn
    Prof. Yanmin Liu Zunyi Normal University yanmin7813@163.com

    Special Session 2: "Swarm Intelligence for Large-scale Discrete Optimization Problems"

    The discrete optimization problem can be seen everywhere in our daily life and attract the attention of a large number of practitioners throughout the year. Swarm intelligence (SI) as an effective method has been successfully applied to such problems and achieved many achievements thanks to its many excellent characteristics, such as simplicity, robustness, parallelism, and applicability. However, classical SI algorithms are also facing new challenges with the rapid increase in the scale of optimization problems, which requires more efficient search mechanism. The objective of this special session is to gather relevant researchers from all over the world and provide a platform for everyone to share your experience and exchange the latest ideas.


    Topics of Interest

    Research areas relevant to the special issue include, but are not limited to, the following topics:

  • This special session receives all original and unpublished work that relates to large-scale discrete optimization problems and SI algorithms in both theoretical analysis and engineering applications.
  • Theoretical analysis of the effectiveness of SI algorithms for discrete problems.
  • New SI algorithms or search strategies for large-scale discrete optimization problems.
  • The mixing of various optimization problems, such as large-scale multi-objective problem and large-scale constrained discrete problem.
  • New discrete benchmark functions or models derived from real-world problems.
  • Applications of SI algorithms to large-scale optimization problems, such as scheduling problem, delivery problem, and mixed discrete-continuous problem

    • Submission


    Please follow the ICSI 2021 instruction for authors and submit your paper via the ICSI 2021 online submission system. Please specify that your paper is for "SS2" -- the Special Session on Swarm Intelligence for Large-scale Discrete Optimization Problems in your paper.

    Organizers


    Dr. Jun YU Niigata University yujun@ie.niigata-u.ac.jp
    Dr. Yan PEI University of Aizu peiyan@u-aizu.ac.jp
    Dr. Kei OHNISHI Kyushu Institute of Technology ohnishi@cse.kyutech.ac.jp

    Special Session 3: "Firework Algorithms of Large-scale Population and Its High Dimensional Optimization Methods"

    In the past few years, the research of fireworks algorithms has developed a wealth of technologies and applications. However, with the development of science and technology, the environment for optimization tasks has changed dramatically. On the one hand, the objective functions are getting larger and larger in dimension and complexity. On the other hand, the parallel capabilities of current computing devices are generally far beyond what they were. Both aspects show great potential of fireworks algorithms with large population.

    The objective of this special session is to gather illuminating works on fireworks algorithms which focus on controlling and scaling the fireworks population. And it also collects instructive applications of fireworks algorithms on high dimensional optimization problems.


    Topics of Interest

    Research areas relevant to the special issue include, but are not limited to, the following topics:

  • All effective fireworks algorithms with larger fireworks populations (at least more than 10 fireworks).
  • Theoretical analysis of effectiveness and scalability of fireworks algorithms.
  • New collaboration mechanism for multiple populations (or fireworks) in optimization.
  • New benchmarking methods of high dimensional optimization problems.
  • New technologies or algorithms (based on fireworks algorithm) for high dimensional optimization problems.
  • Successful application of fireworks algorithms on high dimensional optimization problems.

    • Submission


    Please follow the ICSI 2021 instruction for authors and submit your paper via the ICSI 2021 online submission system. Please specify that your paper is for "SS3" -- the Special Session on Firework Algorithms of Large-scale Population and Its High Dimensional Optimization Methods in your paper.

    Organizers


    Ying Tan Peking University ytan@pku.edu.cn
    Yifeng Li Peking University liyifeng0039@gmail.com
    Maiyue Chen Peking University mychen@pku.edu.cn

    Special Session 4: "Brain Storm Optimization Algorithms"

    The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence algorithm, which is based on the collective behavior of human beings, that is, the brainstorming process. There are two major operations involved in BSO, i.e., convergent operation and divergent operation. A ``good enough'' solution 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 algorithm possesses two kinds of functionalities: capability learning and capacity developing. The divergent operation corresponds to the capability of 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 solutions from the current solution for single-point based optimization algorithms or 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, capacity developing is the search potential possessed by an algorithm. Capability learning is bottom-level learning or micro-level learning. The capability learning describes the ability of an algorithm to find better solutions from current solutions 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 the 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 2021 instruction for authors and submit your paper via the ICSI 2021 online submission system. Please specify that your paper is for "SS4" -- the Special Session on Brain Storm Optimization Algorithms in your paper.

    Organizers


    Lianbo Ma Northeastern University malb@swc.neu.edu.cn
    Xiujuan Lei Shaanxi Normal University xjlei@snnu.edu.cn
    Shi Cheng Shaanxi Normal University cheng@snnu.edu.cn

    Special Session 5: "Intelligent Algorithms for Swarm Robotics and Multibody System"

    Swarm robotics and multibody system are important topics in the robotic systems. Many intelligent algorithms, such as particle swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA), have been used in robotics field. In recent decades, researchers noticed the truth of that ‘Intelligent Algorithms’ will be the soul of many systems and even services. From the technical point of view, intelligent algorithms have gradually appearing in, e.g., computation, modeling, diagnosis, scheduling management, optimization of system integration. Although systems employed with algorithms for robotics have improved performances, it is still a challenge to understand them fully and enhance their intelligence. There are many issues yet worth to be studied for intelligent algorithms in the fields of swarm robotics and multibody system. All of these evoke our research interests.


    Topics of Interest

    The special session aims to collect a series of intelligent algorithms on ideas, concepts, and technologies that are used in swarm robotics and multibody system. Original researches are very welcomed for this session. Potential topics include, but are not limited to:

  • Purely intelligent algorithms research on PSO, ACO, NN, FA, BSO, GA, ABC, AIS, and so on with applications in potential
  • Intelligent algorithms on mobile robotics
  • Intelligent algorithms on multi-mobile manipulators
  • Intelligent algorithms on underwater vehicles
  • Intelligent algorithms for multibody system
  • Intelligent algorithms for multi-robot environmental perception
  • Intelligent algorithms for shape recognition
  • Intelligent algorithms for multibody dynamics

    • Submission


    Please follow the ICSI 2021 instruction for authors and submit your paper via the ICSI 2021 online submission system. Please specify that your paper is for "SS5"-- Intelligent Algorithms for Swarm Robotics and Multibody System in your paper.

    Organizers


    Qirong Tang Tongji University qirong.tang@tongji.edu.cn
    Hai Huang Harbin Engineering University Huanghai@hrbeu.edu.cn

    Special Session 6: "Performance Assessment of Swarm Intelligence Algorithms"

    Solving hard optimization problems is one of the most important research topics due to the countless applications in different areas. Since solving such problems is of great importance, numerous metaheuristics were developed, many of which belong to the group of swarm intelligence (SI) optimization algorithms. In recent decades, there has been an explosion in the number of the proposed swarm intelligence algorithms most commonly compared to other metaheuristics using one statistic such as average or median of a selected performance measure which can lead to putting algorithms in different rankings even though there are only small differences between them. However, providing more insight about SI algorithms performances is still an open issue.

    The focus of this special session is to highlight theoretical and empirical research that investigates approaches needed to analyze SI algorithms and performance assessment with regard to different criteria. The session seeks to bring together researchers from around the globe for stimulating a discussion on recent advances in SI for providing explanations about algorithms’ performance. We invite in particular studies that include benchmarking analyses for SI algorithms.


    Topics of Interest

  • Data-driven approaches (i.e. machine learning, information theory, statistics) for assessing algorithm performance
  • Comparative studies between state-of-the-art SI algorithms
  • Meta-learning
  • Operators influence on SI algorithm behavior
  • Parameters influence on SI algorithm behavior
  • SI algorithm performance prediction
  • Taxonomies of SI algorithms
  • Machine learning for automatic SI algorithm selection and configuration

    • Submission


    Please follow the ICSI 2021 instruction for authors and submit your paper via the ICSI 2021 online submission system. Please specify that your paper is for "SS6"-- Performance Assessment of Swarm Intelligence Algorithms in your paper.

    Organizers


    Dr. Eva Tuba Singidunum University etuba@ieee.org
    Gorjan Popovski Jozef Stefan Institute gorjan.popovski@ijs.si
    Dr. Tome Eftimov Jozef Stefan Institute tome.eftimov@ijs.si
    Prof. dr Milan Tuba Singidunum University, tuba@ieee.org