Special Session 1 : Brain Storm Optimization Algorithms
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” 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 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 or 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 techniquesn
- BSO in uncertain environments
- BSO for real-world applications
Please follow the BIC-TA 2019 instruction for authors and submit your paper via the BIC-TA 2019 online submission system (https://easychair.org/conferences/?conf=bicta2019). When you submit your paper, please specify the topic of your paper as “S1: Brain Storm Optimization Algorithm”.
Hui Lu, Beihang University, China, email@example.com
Yinan Guo, China University of Mining and Technology, China, firstname.lastname@example.org
Shi Cheng, Shaanxi Normal University, China, email@example.com
Special Session 2 : Advances in Swarm Intelligence Algorithms and Real-World Applications
Scope: As a rapidly growing research area, Swarm Intelligence (SI), refers to the collective intelligence of groups of social organisms such as birds, fishes, ants, bees, bacteria, and human beings. The basic operators, the life-cycle principles, the interaction strategies of simple agents with one other, and with their environment can cause a global pattern to emerge and provide insights to management complex systems. Typical SI algorithms include Particle Swarm Optimization, Ant Colony Optimization, Bacterial Foraging Optimization, and Bee Colony Optimization, etc. SI algorithms have already been successfully applied in various fields ranging from social science and ethology to computer science and engineering such as job scheduling, data mining, design optimization, and pattern recognition.
The special session encourages submission of the latest advantages and contributions in theories, technologies, and simulations. Applications of SI algorithms in real-world problems are also welcome.
Research areas relevant to the special issue include, but are not limited to, the following topics:
- Particle Swarm Optimization
- Bacterial Foraging Optimization
- Ant Colony Optimization
- Bee Colony Optimization
- Artificial Fish Search Algorithm
- Harmony Search Algorithm
- Pigeon-Inspired Optimization
- Water Cycle Algorithm
- Other swarm and evolutionary based algorithms
Applications of the above algorithms include but not limited to:
- Operations Research
- Decision Making
- Management Optimization
- Information Systems
- Power and Energy Systems
- Data Mining
- Multi-Objective Optimization
- Pattern Recognition
- Manufacturing System Scheduling
- Intelligent Transportation and Traffic
- Maritime Optimization and Scheduling
- Recommender Systems
- Other relating applications
Please follow the BIC-TA 2019 instruction for authors and submit your paper via the BIC-TA 2019 online submission system (https://easychair.org/conferences/?conf=bicta2019). When you submit your paper, please specify the topic of your paper as “S2: Advances in Swarm Intelligence Algorithms and Real-World Applications”.
Ben Niu, Shenzhen University, China, firstname.lastname@example.org
Hong Wang, Shenzhen University,China, email@example.com
Yuyan Han, Liaocheng University, China, firstname.lastname@example.org
Special Session 3 : Bio-inspired Computation for Feature selection: Theories and Applications
Scope: Feature selection is a key preprocessing technique, commonly used on high-dimensional data. Its purpose is to remove irrelevant/redundant features/attributes from original data, which is able to decrease the complexity of learning algorithms, even improve their performance. However, it is challenging tasks due to the large search space and feature interactions. Due to have global search capability, recent years bio-inspired computation including genetic algorithm, particle swarm optimization, Ant colony optimization, etc, have already been successfully applied in feature selection problems.
This special session aims at presenting the latest developments of Bio-inspired Computation based feature selection, covering ALL different bio-inspired computation paradigms. Research areas relevant to the special issue include, but are not limited to, the following topics:
- Particle swarm optimization for feature selection
- Ant colony optimization for feature selection
- Bee colony optimization for feature selection
- Differential evolution for feature selection
- Genetic algorithm for feature selection
- Pigeon-inspired optimization for feature selection
- Other swarm and evolutionary algorithms for feature selection
- Applications of the above algorithms in real problems
Please follow the BIC-TA 2019 instruction for authors and submit your paper via the BIC-TA 2019 online submission system (https://easychair.org/conferences/?conf=bicta2019). When you submit your paper, please specify the topic of your paper as “S3: Bio-inspired Computation for Feature selection: Theories and Applications”.
Yong Zhang, China University of Mining and Technology, China, email@example.com
Yu Xue, Nanjing University of Information Science & Technology, China, firstname.lastname@example.org
Fei Han, Jiangsu University, China, email@example.com