Alternating current operation with one full cycle at a plasma current level
of 2.5 kA has been achieved in the CT-6B tokamak. The poloidal magnetic field
in the plasma is measured with two internal magnetic probes in repeated
discharges. The plasma current distribution is reconstructed with an
inversion algorithm. The reversed plasma current first appears on the low
field side due to decreasing poloidal beta. Two plasma current components
flow in opposite directions when the net current vanishes.
The existence of the magnetic surfaces and rotational
transform provides particle confinement in the current reversal phase.
Large-scale network plan optimization of resource-leveling with a fixed duration is challenging in project management. Particle swarm optimization (PSO) has provided an effective way to solve this problem in recent years. Although the previous algorithms have provided a way to accelerate the optimization of large-scale network plan by optimizing the initial particle swarm, how to more effectively accelerate the optimization of large-scale network plan with PSO is still an issue worth exploring. The main aim of this study was to develop an accelerated particle swarm optimization (APSO) for the large-scale network plan optimization of resource-leveling with a fixed duration. By adjusting the acceleration factor, the large-scale network plan optimization of resource-leveling with a fixed duration yielded a better result in this study than previously reported. Computational results demonstrated that, for the same large-scale network plan, the proposed algorithm improved the leveling criterion by 24% compared with previous solutions. APSO proposed in this study was similar in form to, but different from, particle swarm optimization with contraction factor (PSOCF). PSOCF did not have as good adaptability as APSO for network plan optimization. Accelerated convergence particle swarm optimization (ACPSO) is similar in form to the APSO proposed in this study, but its irrationality was pointed out in this study by analyzing the iterative matrix convergence.
No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).
Alternating current operation with one full cycle and a current level of 2.5 k A have been achieved in the CT-6B tokamak. The poloidal magnetic field in the plasma is measured with two internal magnetic probes in repeated discharges. The current distribution is reconstructed with an inversion algorithm. The inversed current first appears on the weak field side. The existence of magnetic surfaces and rotational transform provide particle confinement in the current reversal phase.
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