Particle swarm optimization (PSO) is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate the applicability of PSO to inversion of geophysical data, we inverted three noise-corrupted synthetic sounding data sets over a multilayered 1D earth model by using DC, induced polarization (IP), and magnetotelluric (MT) methods. The results show that acceptable solutions can be obtained with a swarm of about 300 particles and that convergence occurs in less than 100 iterations. The time required to execute a PSO algorithm is comparable to that of a genetic algorithm (GA). Similarly, the models estimated from PSO and GA are close to the true solutions. Whereas a ridge regression (RR) algorithm converges in four to eight iterations, it yields satisfactory results only when the initial model is very close to the true model. Models estimated from PSO explain observed, vertical electric sounding (VES) and MT data, from Bhiwani district, Haryana, India, and the Chottanagpur gneissic complex, Dhanbad, India. The results are consistent with RR and GA inversions.
A Low Voltage Governance and Optimization Strategy for Distribution Network Based on Multi-objective Particle Swarm Optimization Algorithm
