Large-Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

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)

Accelerated Particle Swarm Optimization to Solve Large-Scale Network Plan Optimization of Resource-Leveling with a Fixed Duration

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

Research on Deterioration Mechanism of Concrete Materials in an Actual Structure

The cause for deterioration of the concrete structure located in severe environment has been explored both in field and in laboratory. Serious cracking and spalling appeared upon surface of the concrete structure soon after the structure was put into service. Both alkali-aggregate reaction and freeze-thaw cycles may result in similar macro visible cracking and spalling. The possibility of alkali-aggregate reaction was excluded by both field survey and lab examination such as chemical analysis, petrographic analysis, and determination of alkali reactivity of aggregates. According to results of freeze-thaw cycles, impermeability testing, and microstructure analysis, it is deduced that the severe environmental conditions plus the relatively inferior frost resistance cause the deterioration of concrete. Usage of air entraining admixture can improve frost resistance and impermeability. Furthermore, new approaches to mitigate the deterioration of concrete used in severe environmental condition are discussed