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The purpose of this paper is to extend prior supply chain research by describing the process of innovation knowledge increase in supply chain network. More specifically, this study investigates the role of network density, and views the knowledge increase as the process of knowledge diffusion and knowledge innovation.

A multi-agent model, which demonstrates the process of knowledge increase in supply chain network, was established, and simulated by using NetLogo simulation platform.

The results indicate that the network density will promote the knowledge increase of the supply chain when it is high or low. In the meantime, these results show that the inhibition of knowledge diffusion and knowledge innovation will appear when network density is moderate.

Although previous research has identified the importance of knowledge increase in promoting sustainable development of supply chain, far less attention was given to the study of the effect of network structure on the knowledge increase in supply chain. This study thus fulfills the research gap by providing a description of the process of knowledge increase with the consideration of network density. The conclusion is of great significance for the choice of network density for sustainable development of supply chain.

The purpose of this paper is to deal with the economic requirements of power system loading dispatch and reduce the fuel cost of generation units. In order to optimize the scheduling of power load, an improved chicken swarm optimization (ICSO) is proposed to be adopted, for solving economic load dispatch (ELD) problem.

The ICSO increased the self-foraging factor to the chicks whose activities were the highest. And the evolutionary operations of chicks capturing the rooster food were increased. Therefore, these helped the ICSO to jump out of the local extreme traps and obtain the global optimal solution. In this study, the generation capacity of the generation unit is regarded as a variable, and the fuel cost is regarded as the objective function. The particle swarm optimization (PSO), chicken swarm optimization (CSO), and ICSO were used to optimize the fuel cost of three different test systems.

The result showed that the convergence speed, global search ability, and total fuel cost of the ICSO were better than those of PSO and CSO under different test systems. The non-linearity of the input and output of the generating unit satisfied the equality constraints; the average ratio of the optimal solution obtained by PSO, CSO, and ICSO was 1:0.999994:0.999988. The result also presented the equality and inequality constraints; the average ratio of the optimal solution was 1:0.997200:0.996033. The third test system took the non-linearity of the input and output of the generating unit that satisfied both equality and inequality constraints; the average ratio was 1:0.995968:0.993564.

This study realizes the whole fuel cost minimization in which various types of intelligent algorithms have been applied to the field of load economic scheduling. With the continuous evolution of intelligent algorithms, they save a lot of fuel cost for the ELD problem.

The ICSO is applied to solve the ELD problem. The quality of the optimal solution and the convergence speed of ICSO are better than that of CSO and PSO. Compared with PSO and CSO, ICSO can dispatch the generator more reasonably, thus saving the fuel cost. This will help the power sector to achieve greater economic benefits. Hence, the ICSO has good performance and significant effectiveness in solving the ELD problem.