Total Optimization of Smart City by Multi-population Brain Storm Optimization with Differential Evolution Strategies

This paper proposes a total optimization method of a smart city (SC) by multi-population brain storm optimization with differential evolution strategies (MP-BSODE). Energy cost, actual electric power loads at peak load hours, and CO2 emission are minimized using a SC model. Many evolutionary computation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), and Differential Evolutionary Particle Swarm Optimization (DEEPSO) have been applied to the problem. However, there is room for improving solution quality. The results by the proposed MP-BSODE based method are compared with those by the original brain storm optimization with differential evolution strategies (BSODE), DEEPSO, and the original BSO based methods through simulations applied to a model of Toyama city, which is a moderately-sized city in Japan.