Modeling of Coupled Saturated Steam-Water Spaces Using Quasi-steady State, Quasi-numerical Approach

摘要

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. This lecture presents the latest advancement of Particle Swarm Optimization (PSO) in asynchronous update, discrete, and multi-objective problems. PSO is a population based stochastic optimization algorithm, inspired by the social behavior of bird flocking and fish schooling. PSO has been introduced by Kennedy and Eberhart and contains a group of particles that move in a search space searching for an optimum solution according to a particular objective function. The movement of a particle is subjected to its own best found solution, pBest, and the best found solution in the neighborhood, gBest.