Here it is called the MAS-DGA system.
Benchmark problems have been tested in the experiments, and the results are compared with a symbiotic evolutionary algorithm (SEA) and a cooperative co-evolutionary genetic algorithm (CCGA), which reveals that the proposed MAS-DGA system is feasible and efficient for the resolve of IPPS problems.
In this study, a DC microgrid is considered as an MAS, which includes distributed generator agents (DGAs), energy storage system agents (ESSAs) and load agents (LAs).
DO-DGA applies modified DGA, including different mutation schemes, to obtain the global optimum solution.
Increasing the sensing range decreases the active sensor nodes on both the TCDC and DGA algorithms; however, our TCDC system provides more than 50%% performance improvement when the sensing range is doubled (from 30 to 60 m). Figure 11 b shows that the sensing coverage increases with the sensing range in both the algorithms, as expected theoretically.
Considerable care is required in order to be fit for a DGA, including fasting before the procedure, navigating oneself through the hospital system and establishing follow-up care (driving immediately after DGA treatment is not permitted).
It is also evident from all the graphs of Fig. 7 that the confidence interval of our proposed TCDC system is much lower compared to that of the DGA and ACDA systems, i.e., the TCDC system shows better performance stability compared to them.
Our proposed TCDC system gives much better performance than the ACDA and DGA algorithms.
Our TCDC system achieves better lifetime compared to the ACDA and DGA algorithms for the following reasons.
Our proposed TCDC system gives much better performance than both the ACDA and DGA algorithms.
In this paper, a distributed genetic algorithm (DGA) is suggested to cater for the IPPS problems domains, and the multi-agent system (MAS) is adopted to accommodate the algorithm.
