ISMSI 2027 (International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence) is an academic conference held in Ho Chi Minh City, Vietnam on 2027-04-23. The paper submission deadline is 2026-11-25. Acceptance notifications are sent on 2026-12-25.
Topics of interest for submission include, but are not limited to:
Track 1: Swarm Intelligence and Nature-Inspired Optimization Algorithms
Cuckoo Search Algorithm
Harmony Search Algorithm
Wolf Search Algorithm
Elephant Search Algorithm
Monarch Butterfly Algorithm
Particle Swarm Optimization Algorithm
Artificial Bee and Firefly Algorithms
Bacterial Foraging Optimization Algorithm
Ant Colony Optimization Algorithm
Swarm Intelligence Algorithm
Firefly Swarm Optimization Algorithm
Review and Comparative Study of Swarm Intelligence Technologies
Theory and Practice of Swarm Intelligence Methods in Different Fields
Application of Swarm Intelligence Methods in Practical Problems
Swarm Robotics Technology
Gray Wolf Optimizer
Whale Optimization Algorithm
Hybrid Swarm Intelligence Algorithm
Multi-Objective Swarm Intelligence Optimization
Adaptability and Robustness of Swarm Intelligence Algorithms in Dynamic Environments
Convergence, Complexity, and Theoretical Analysis of Swarm Intelligence Algorithms
Track 2: Evolutionary Computation Algorithms and Models
Evolutionary Dynamics
Memetic Theory
Evolutionary Algorithms
Memetic Algorithms
Genetic Algorithms: Theory, Technology, and Applications
Genetic Programming: Tree, Linear, Cartesian, and Other Representations
Evolutionary Strategies and Evolutionary Programming
Differential Evolution
Coevolution
Multi-Objective Evolutionary Algorithms (e.g., NSGA-II/III, MOEA/D, SPEA2, etc.
Estimation distribution algorithms
Met algorithms and hybrid evolutionary algorithms
Constraint handling techniques in evolutionary computation
Evolutionary dynamics and evolvability theory analysis
Evolutionary deep learning
Quantum computing-inspired evolutionary and swarm algorithms
Cellular genetic algorithms
Interactive evolutionary computation
Track 3: Hybrid Metaheuristic Algorithms and Local Search Methods
Hybrid (parallel) metaheuristic algorithms, such as:
Tabu search algorithm
Path reconnection algorithm
Scattering algorithm search
GRASP Methods
Iterative Local Search
Simulated Annealing
Variable Neighborhood Search
Constrained Optimization
Landscape Analysis
Convergence Theory and Mathematical Analysis of Metaheuristic Algorithms
Modeling Metaheuristic Algorithms Based on Dynamical Systems and Markov Chains
Theoretical Framework for Large-Scale, High-Dimensional, and Sparse Optimization
Adaptive Control of Metaheuristic Parameters and Operators Based on Reinforcement Learning
Hyperheuristic Algorithms: Management of the Underlying Algorithm Pool Based on Selection and Generation
Metaheuristic Algorithms Inspired by Quantum Computing
Distributed Metaheuristics in a Cloud-Edge Collaborative Computing Framework
Track 4: Swarm Intelligence and Collaborative Control
Autonomous Agents and Multi-Agent Reinforcement Learning
Competition and Evolution among Agents
Applications of Swarm Intelligence in Major Projects Such as Smart Cities and Environmental Monitoring
Interaction between Brain-Computer Interfaces and Swarm Intelligence Systems
Collaborative Control and Self-Organization of Swarm Robots
Robot Path Planning and Navigation
Evolutionary Robotics: Coevolution of Morphology and Control
Multi-Robot Task Allocation and Formation Control
Collaborative Search and Tracking of Drone Swarms
Intelligent Traffic Flow Control and Guidance Based on Swarm Intelligence
Closed-Loop Interaction between Brain-Computer Interfaces and Swarm Intelligence Systems
Robustness of Swarm Intelligence Systems Under Adversarial Attacks
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