Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization
Mayuko Sato, Yoshikazu Fukuyama, Mohammed El-Abd, Tatsuya Iizaka, Tetsuro Matsui · 2019
This paper proposes total optimization of energy networks in a smart city (SC) by cooperative coevolution using global-best brain storm optimization (CCGBSO). The smart city problem is one of mixed integer nonlinear programming (MINLP) problems. Therefore, various evolutionary computation methods such as differential evolutionary particle swarm optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (GBSO) have been applied to the problem. However, quality of solution is still required to be improved. Cooperative Cooperation has a possibility to improve solution quality of large scale optimization problems such as the SC problem and this paper proposes a new cooperative coevolution algorithm, CCGBSO. The results of the proposed CCGBSO based method are verified to be the most improved comparing with those of the conventional DEEPSO, BSO, MBSO, and GBSO based methods.