Conference Information

ISMSI 2027: International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

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Submission Date:
2026-11-25 Due in 164 days
Notification Date:
2026-12-25
Conference Date:
2027-04-23
Location:
Ho Chi Minh City, Vietnam
Years:
11
Viewed: 17250   Tracked: 5   Attend: 3

Call For Papers

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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