Complex Optimization Problems Using Highly Efficient Particle Swarm Optimizer
Abstract: Many engineering
problems are the complex optimization problems with the large numbers of global
andlocal optima. Due to its complexity, general particle swarm optimization
method inclines towards stagnation phenomena in the later stage of evolution,
which leads to premature convergence. Therefore, a highly efficient particle
swarm optimizer is proposed in this paper, which employ the dynamic transitionstrategy
ofinertia factor, search space boundary andsearchvelocitythresholdbased on
individual cognitionin each cycle to plan large-scale space global search and
refined local search as a whole according to the fitness change of swarm in
optimization process of the engineering problems, and toimprove convergence
precision, avoid premature problem, economize computational expenses, and
obtain global optimum. Several complex benchmark functions are used to testify
the new algorithm and the results showed clearly the revised algorithm can
rapidly converge at high quality solutions.
Author: Kaiyou Lei
Journal Code: jptkomputergg140123
