Enhanced Particle Swarm Optimization Algorithm for Solving Economic Dispatch Problems

The enhanced Particle Swarm Optimization (PSO) algorithm demonstrates superior efficiency over traditional methods when solving economic dispatch problems. Essentially, economic dispatch involves optimally distributing power generation among various units while satisfying operational constraints to minimize total generation costs. Such problems typically feature high dimensionality, nonlinearity, and multiple constraints, where conventional mathematical programming approaches often converge to local optima or suffer from high computational complexity.

The standard PSO algorithm mimics bird flock foraging behavior to search for optimal solutions, where each particle represents a potential solution and updates its position by tracking both individual historical best and global historical best positions. However, in economic dispatch scenarios, standard PSO tends to suffer from premature convergence and insufficient search precision. The enhanced version implements three key improvements: First, dynamic inertia weighting is introduced - maintaining higher values during initial iterations to enhance global exploration capability, then gradually decreasing to improve local exploitation accuracy (implemented as w = w_max - (w_max - w_min) * iter/max_iter). Second, nonlinearly varying acceleration coefficients balance the influence of individual experience versus swarm intelligence (typically implemented using time-varying cognitive and social parameters). Finally, constraint handling techniques like penalty function methods or feasible solution preservation strategies ensure compliance with engineering constraints such as power balance and generator output limits.

Experimental data confirms significant improvements in both convergence speed and solution quality, particularly when handling complex economic dispatch models with valve-point effects and multi-fuel options. This intelligent optimization approach provides new solution pathways for power system economic operations, and its core concepts can be extended to resource optimization problems in other domains. Key algorithm components include position update functions (x_i(t+1) = x_i(t) + v_i(t+1)) and velocity calculation methods incorporating the enhancement mechanisms.