Dynamic Particle Swarm Optimization Algorithm

This paper discusses the application of the mainstream Particle Swarm Optimization (PSO) algorithm for optimization in dynamic environments. The algorithm employs position and velocity update equations: v_i(t+1) = w*v_i(t) + c1*r1*(pbest_i - x_i(t)) + c2*r2*(gbest - x_i(t)) and x_i(t+1) = x_i(t) + v_i(t+1), where w represents inertia weight, c1 and c2 are acceleration coefficients. In dynamic environments, the algorithm incorporates environmental change detection mechanisms and response strategies, including re-initialization of particles or preservation of diversity through techniques like quantum-behaved PSO or multi-swarm approaches. The algorithm continuously adjusts its parameters and optimization strategies based on environmental changes, utilizing fitness reevaluation and elitism preservation to maintain solution quality. This adaptive capability enables the algorithm to effectively handle changing conditions and converge toward optimal solutions, demonstrating significant potential for various applications including robotic path planning, dynamic scheduling, and real-time control systems.