Total Optimization of Energy Networks in a Smart City by Multi-Swarm Differential Evolutionary Particle Swarm Optimization

This paper proposes total optimization of energy networks in a smart city (SC) by multi-swarm differential evolutionary particle swarm optimization (MS-DEEPSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and SC demonstration projects have been conducted around the world for reducing total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization, differential evolution, and DEEPSO have been applied to the problem. However, there is still room for improving solution quality. Multi-swarm based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. Considering these backgrounds, the paper proposes total optimization of energy networks in an SC by MS-DEEPSO with only migration model and with only abest model. The results of MS-DEEPSO with both migration and abest, only migration, and only abest model based methods are compared with those by the original DEEPSO based method with a single swarm. Various migration topologies, migration policies and intervals, and the number of sub-swarms are also investigated. It is verified that MS-DEEPSO with both migration and abest model based method with hyper-cube topology and the W-B policy is the most effective among all of multi-swarm parameters.