Thuật ngữ AI
Từ điển đầy đủ về Trí tuệ nhân tạo
Particle
Individual agent in the swarm representing a potential solution, characterized by its position in the search space and its movement velocity.
Swarm
Population of particles that collectively interact to explore the search space and converge toward optimal solutions through their social behavior.
Velocity
Displacement vector of a particle in the search space, updated at each iteration based on its personal best position and the global best position.
Position
Coordinates of a particle in the search space representing a specific solution to the optimization problem being addressed.
Personal Best Position
Best solution found by an individual particle since the beginning of the algorithm, serving as local memory to guide its future movements.
Global Best Position
Best solution discovered by the entire swarm, used as a reference point to attract all particles toward optimal regions.
Inertia Coefficient
Parameter controlling the influence of a particle's previous velocity, allowing for a balance between global exploration and local exploitation in the search.
Acceleration Coefficients
Parameters c1 and c2 weighting the influence of the personal best position and the global best position respectively on particle movement.
Neighborhood
Subset of particles with which a given particle shares information, defining the communication structure within the swarm.
Neighborhood Topology
Connection structure between particles determining how information flows in the swarm, influencing convergence speed and solution diversity.
Convergence
Process by which swarm particles gradually tend towards a common region of the search space, indicating the stabilization of solutions.
Constriction Factor
Multiplicative parameter ensuring algorithm convergence by controlling the amplitude of particle oscillations around optimal solutions.
Diversification
Algorithm's ability to explore different regions of the search space to avoid local optima and discover new promising solutions.
Intensification
Research phase concentrated around already discovered promising solutions to refine and improve the quality of local solutions.
Objective Function
Mathematical function evaluating the quality of each particle position, serving as a criterion to guide swarm evolution towards optimal solutions.
Search Space
Multidimensional domain containing all possible solutions of the optimization problem, in which particles move to find the optimum.