Unit Commitment Computation - A Novel Fuzzy Adaptive Particle Swarm Optimization Approach

This paper presents a fuzzy adaptive particle swarm optimization (FAPSO) for unit commitment (UC) problem. FAPSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable resource size and changing load demand in deregulated market, the fuzzy adaptive criterion is applied for the PSO inertia weight based on the diversity of fitness. In this method, the inertia weight is dynamically adjusted using the fuzzy IF/THEN rules. To increase the knowledge, the global best location is moved instead of a fixed one in each generation. To avoid the method to be frozen, stagnated/idle particles are reset from time to time. Velocity is digitized (0/1) by a logistic function for the binary UC schedule. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method