Particle swarm optimization
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About
Particle swarm optimization (PSO) is a population-based computational method inspired by the collective movement of biological swarms, such as flocks of birds or schools of fish. First introduced in the mid-1990s, PSO works by maintaining a population of candidate solutions, called particles, that fly through a solution space, adjusting their positions based on their own best-known location and the best-known location discovered by the entire swarm. This social-cognitive balance allows the algorithm to efficiently explore complex, high-dimensional search spaces without requiring gradient information. In robotics and AI, PSO is widely applied to path planning for mobile robots and UAVs, controller tuning for manipulators, assembly line balancing, and training fuzzy logic systems. Its ability to handle nonlinear, multi-objective optimization problems with relatively few parameter adjustments makes it practical across diverse hardware and simulation environments. PSO matters because it offers a computationally efficient alternative to classical optimization and genetic algorithms, consistently delivering smooth, near-optimal solutions in real-world robotics challenges where speed and adaptability are critical.
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