Tatsuya Iizaka

The authors wish to make the following corrections to their paper

This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO₂ emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO₂ emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.

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 (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), 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. The proposed MS-DEEPSO utilizes both migration and abest, which is the best searching point so far among all sub-swarms. The results of the proposed method are compared with those by the original DEEPSO with a single swarm, MS-DEEPSO with only migration, and with only abest. Various migration topologies, migration policies and intervals, and number of sub-swarms are also investigated. It is verified that the proposed MS-DEEPSO with hyper-cube topology and the W-B policy is the most effective among all of multi-swarm parameters.