Resumen
This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Many countries have conducted smart city demonstration projects for reduction of total energies and CO2 emission. The energy and environmental problem of smart city can be formulated as a mixed integer nonlinear programming (MINLP) problem. Therefore, evolutionary computation methods including variations of recently developed Brain Storm Optimization (BSO) such as Global-best BSO (GBSO), Modified BSO (MBSO), and Global-best Modified BSO (GMBSO) have been adopted to the problem. However, there is still room for improvement of quality of solution. Evolutionary computation methods with multi-population have been applied to various problems and verified to improve quality of solution. Therefore, the approach can be expected to improve quality of solution. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest which is the best individual among all sub-populations so far and both migration and abest. Various multi-population models, migration policies, the number of sub-populations, and migration topologies are also investigated. It is verified that the proposed MP-GMBSO based method with migration using ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.