Call for Symposia Proposals for NTCI 2026
The International Conference on New Trends in Computational Intelligence (NTCI)has been held for 6 sessions since 2016, with over 5,000 participants in total. It serves as a pivotal international conference in the field of computational intelligence, garnering significant attention from researchers. A total of 53 top experts, including academicians, IEEE Fellows, and journal editors in the field of computational intelligence from around the globe, have delivered 78 invited reports. Since 2023, the conference has achieved stable EI indexing, with more than 200 academic papers included to date.It provides a platform for researchers, practitioners, and scholars in computational intelligence to focus on discussing new fields, designs, and applications, having become an influential international platform in the field.
The 8th International Conference on New Trends in Computational Intelligence (NTCI 2026) will take place in Jin'zhou, China from November 6 to 8, 2026. To ensure the conference's high-quality execution, encourage active participation from researchers and engineering professionals, and comprehensively showcase new advancements and research achievements in computational intelligence and related fields at home and abroad, the conference now invites proposals for NTCI 2026 symposia from the entire industry. Relevant units and experts are cordially invited to support and undertake the symposia!
I. Overview of NTCI 2026 Symposia
(1) Symposia Theme Directions
Symposia themes can be planned around directions such as models and algorithms(e.g., machine learning, multi-objective optimization, image processing, quantum machine learning, etc.), and theories and applications(e.g., space information, smart healthcare, IoT engineering, material genome engineering, network information security, intelligent oil & gas field development, intelligent marine engineering, intelligent operations research & optimization, etc.).
(2) Symposia Forms
NTCI 2026 symposia support diverse formats, including but not limited to special report sessions, round-table dialogues, group discussions, and interactive seminars. All symposia will be held offline.
(3) Symposia Schedule
NTCI 2026 will be held from November 6 to 8, 2026, with symposia conducted concurrently with the main conference.
(4) Symposia Venue
Offline venues for all symposia will be the same as the main conference venue in Jin'zhou.
II. Undertaking Requirements
(1) Applicant units/individuals must have relevant work experience, strong resource mobilization and conference organization capabilities, and actively cooperate with the conference's overall arrangements.
(2) Each symposia may set its own theme and appoint 1-3 symposium chairs, who should possess influence and prestige in the relevant field.
(3) Each symposia must solicit at least 5 contributions (long abstracts or papers, with at least 4 being papers), and chairs may invite one Keynote report.
III. Organizing Committee Support
The Organizing Committee will provide venue services, financial support, on-site facilities, and other resources, and recommend outstanding papers to SCI journals.
IV. Additional Notes
(1) For unmentioned matters, please consult the Organizing Committee.
(2) The Organizing Committee reserves the right to interpret these symposia application guidelines.
(3) For specific matters of symposia application, please consult the organizing committee in advance (Ms. Wang: +86-13061345702; Ms. Yang: +86-15053818666).
Symposia for NTCI 2026
Symposium Overview:This special session centers on bio-inspired intelligence and brain-mimicking bionic systems, integrating computational neuroscience, computational biology, neuroscience-inspired artificial intelligence, biologically plausible neural networks, biological neural networks, biological visual computing, biomimetic image processing, embodied intelligence, insect navigation, bio-robots, swarm intelligence and swarm robotics. Conventional artificial intelligence models lack biological interpretability, and their perception, autonomous navigation and swarm coordination capabilities lag far behind biological brains and natural swarms. Critical challenges include insufficient biological plausibility of brain-like networks, poor environmental adaptability of biomimetic vision, low efficiency in autonomous decision-making and swarm cooperation of embodied robots, and weak navigation robustness of miniature insect-like robots. We invite original worldwide submissions covering theoretical modeling of brain-inspired neural networks, bionic visual perception and image algorithms, neural mechanism simulation of insect navigation, development of embodied bio-robots, as well as theories, algorithms and physical systems of swarm intelligence and coordinated swarm robotics. This session promotes interdisciplinary innovation across neuroscience, biology, artificial intelligence and robotics, and builds an academic exchange platform for global researchers.
Topics of Interest Include (but not limited to):
1. Theoretical improvement and performance optimization of conventional machine learning models (SVM, random forest, ensemble learning, etc.)
2. Architecture design, lightweight implementation, regularization and training optimization of deep neural networks
3. Loss function and parameter optimization for CNN, RNN, Transformer and other neural networks
4. Improvements of classic swarm intelligence algorithms: PSO, ACO, ABC, GWO, SSA, WOA and more
5. Novel swarm intelligence optimizers for multi-objective, dynamic, constrained and high-dimensional optimization problems
6. Hybrid frameworks integrating neural networks and swarm intelligence (hyperparameter tuning via intelligent algorithms, Neural Architecture Search/NAS)
7. Federated collaborative swarm intelligence optimization and distributed machine learning training
8. Swarm intelligence based feature selection, data denoising and adaptive preprocessing
9. Hybrid machine learning-swarm intelligence modeling for small-scale and imbalanced datasets
10. Optimization strategies for multi-modal data fusion learning
11. Applications of hybrid swarm intelligence-neural network models in intelligent manufacturing, medical imaging and financial forecasting
12. Intelligent decision-making and path planning via reinforcement learning integrated with swarm intelligence
13. Meta-heuristic swarm intelligence for automated machine learning (AutoML)
14. Machine learning based intelligent solutions for engineering scheduling, route planning and resource allocation
15. Neuroscience-Inspired Artificial Intelligence, Biologically Plausible Neural Networks and Biomimetic Visual Computing
16. Embodied Intelligence, Insect Navigation and Bio-Inspired Swarm Robotics
Session Chairs：
◆ Prof. Qinwei Fan, Guangzhou University, Guangzhou, China
◆ Prof. Xuelong Sun, Guangzhou University, Guangzhou, China
◆ Prof. Qinbin Fu, Guangzhou University, Guangzhou, China
◆ A/Prof. Mingshuo Xu, University of Leicester, Leicester, UK
Telephone:13186106401
Special Session Invitation Code: PJGZ
Symposium Overview: Group games and intelligent control provide a modeling strategic interaction framework for distributed coordination and adaptive decision-making in large-scale networked systems. The field studies how multi-agents cooperate or compete under uncertainty, limited information, constraints, and dynamic environments, with applications in robotics, transportation, energy networks, and cyber-physical systems. By combining game theory, control theory, optimization, and machine learning, researchers seek equilibrium strategies, stable coordination mechanisms, and scalable control laws for interconnected agents. Recent progress has improved robustness, autonomy, and coordination in complex systems where centralized methods are difficult to deploy. The symposium focuses on theoretical and practical studies of modeling, analysis, computation, and implementation for group games and intelligent control. It pays attention to equilibrium computation, cooperative‑competitive learning, and coordinated control for real‑world multi‑agent applications. It aims to advance the interdisciplinary development of relevant findings within the fields of mathematics, control, computer science and engineering.
Topics of Interest Include (but not limited to):
1.Group game theory for multi-agent and networked systems
2.Intelligent control based on neural networks and fuzzy systems
3.Differential games, dynamic games, and evolutionary games
4.Distributed optimization and game strategy
5.Fault diagnosis, isolation and fault-tolerant control
6.Learning-based multi-agent system optimization control
7.Formation control, tracking control, and containment control
8.Cooperative-competitive decision-making and resource allocation
9.Game-theoretic scheduling, planning, and task assignment
10.Applications in robotics, transportation, energy systems, and cyber-physical systems
Session Chairs：
◆ Yi Peng, Professor, Tongji University, Shanghai, China
◆ Yuezu Lü, Professor, Beijing Institute of Technology, Beijing, China
◆ Wei Wang, Professor, Liaoning University of Technology, Jinzhou, Liaoning, China
◆ Lei Liu, Professor, Liaoning University of Technology, Jinzhou, Liaoning, China
◆ Qiang Zeng, Associate Professor, Liaoning University of Technology, Jinzhou, Liaoning, China
Telephone:18840176734
Special Session Invitation Code: YLLZ
Session Overview: This session focuses on the interdisciplinary frontiers of data-driven intelligent perception, control, and optimization. Taking the representation, fusion, measurement, and optimization of data as the central theme, it integrates research directions such as sparse data representation, multi-view data fusion, low-rank tensor learning, neurodynamic optimization, memristive system dynamics, inertial neural network control, distributed optimization, and games. The research of the five session organizers covers the complete technical chain from data-driven perception (multi-view low-rank tensor feature selection, sparse subspace learning) to data-driven optimization (neurodynamic distributed algorithms, nonsmooth game solving) and data-driven control (memristive chaotic system control, inertial neural network impulsive synchronization). Unlike related sessions such as swarm intelligence metaheuristics, information-driven modeling, pattern recognition, and engineering scheduling, this session takes structured data representation and dynamical system convergence as its core theoretical tools. It targets complex operating conditions such as high dimensionality, noise, time delay, and resource constraints, and carries out an integrated closed-loop design of perception–optimization–control. The session solicits original research results from around the world, covering theories, algorithms, and system implementations in data-driven sparse perception and feature selection, neurodynamic optimization, memristive and inertial system control, distributed optimization and games, etc., to promote interdisciplinary innovation across data science, optimization theory, control science, and artificial intelligence, and to build a high-quality academic exchange platform for scholars at home and abroad.
Topics of Interest Include (but not limited to):
1. Data-driven sparse perception and representation: sparse coding, compressed sensing, low-rank sparsity, structured sparsity, sparse Bayesian learning, multi-view unsupervised feature selection, subspace learning, and low-rank tensor constraints.
2. Data fusion and multimodal perception: multimodal data fusion and collaborative perception involving vision, radar, IMU, touch, etc.; information bottleneck and information geometry methods.
3. Data-driven optimization theory and algorithms: convex/nonconvex sparse optimization, L1 regularization, distributed sparse optimization, nonsmooth optimization, and generalized gradient methods.
4. Neurodynamic optimization: continuous/discrete neurodynamic algorithms, projection neural networks, recurrent neural networks, Hopfield networks, and distributed neurodynamic algorithms for solving constrained optimization and game problems.
5. Memristive systems and data processing: memristive neural network dynamics, memristive chaotic systems, and stability and synchronization analysis of memristive inertial neural networks.
6. Inertial neural networks and impulsive control: modeling of delayed inertial neural networks, impulsive control theory and applications, intermittent control, and stability analysis of hybrid systems.
7. Data-driven intelligent control: nonlinear control, complex network control, robust control, adaptive control, sliding mode control, and finite-time/fixed-time control.
8. Distributed optimization and games: distributed algorithms for solving nonsmooth aggregative games, constrained distributed interval-valued optimization, federated optimization, and multi-agent coordination.
9. Data-driven perception and control methods under small-sample, imbalanced, noisy, and time-delay conditions.
10. Engineering applications: intelligent manufacturing, medical imaging, smart transportation, energy systems, robotics, and signal and image processing.
Session Chairs：
◆ Junjian Huang, Professor, Southwest University, Beibei District, Chongqing, Tel: +86 18602311533
◆ Wei Zhang, Associate Professor, Southwest University, Beibei District, Chongqing
◆ You Zhao, Lecturer, Southwest University, Beibei District, Chongqing
◆ Hongfei Li, Associate Professor, Southwest University, Beibei District, Chongqing
◆ Jingxin Liu, Lecturer, Chongqing University of Science and Technology, Shapingba District, Chongqing
Special Session Invitation Code: SWUH