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Huang Y.,Tongji University | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | Wu Q.,Tongji University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Home energy management system is an important part in smart home, smart home is assumed to be equipped with smart meter which smart control of generators, storages, and demand response programs. In this paper, we study a versatile convex optimization framework for the automatic energy management of various household loads in a smart home. The scheduling algorithm determines how energy resources available to the end-users considering a number of constraints. Hence, a hybrid PSO-DE algorithm approach is proposed. We devise a model accounting for a typical household user, and present computational results showing that it can be efficiently solved in real-life instances. © Springer International Publishing Switzerland 2014.


Cai X.,Tongji University | Cai X.,Taiyuan University of Science and Technology | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | And 2 more authors.
International Journal of Wireless and Mobile Computing | Year: 2015

Bat algorithm is a novel swarm intelligent algorithm inspired by the echolocation behaviour of bats. Pulse rate of emission is one important parameter, which is adjusted in an exponential manner in the standard version, however, this manner provides a fast convergent behaviour in the first several generations. With this manner, the exploitation capability is limited. In this paper, a new linear adjusted manner is employed to enhance the local search capability. To test the performance, it is applied to solve the optimal coverage problem of Wireless Sensor Network (WSN). Simulation results show it is effective when compared with the standard version of bat algorithm. Copyright © 2015 Inderscience Enterprises Ltd.


Cai X.,Tongji University | Cai X.,Taiyuan University of Science and Technology | Li W.,Tongji University | Li W.,Jiaxing Vocational Technical College | And 4 more authors.
Journal of Bionanoscience | Year: 2014

Bat algorithm is a novel swarm intelligent algorithm inspired by the echolocation behavior of bats with varying pulse rates of emission and loudness. In this paper, a new variant which is called adaptive bat algorithm is designed and applied to solve the toy model prediction problem. In this algorithm, a linearly dynamic pulse rate selection strategy is designed. To test the performance, short sequences, Fibonacci sequence and real protein sequences are selected to compare, simulation results show adaptive bat algorithm is validity. Copyright © 2014 American Scientific Publishers.


Guo Y.,Tongji University | Kang Q.,Tongji University | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | Wu Q.,Tongji University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper presents a multivariable grey neural network (MGM-NN) model for predicting the state of industrial equipments. It combines the merit of MGM model and RBF-NN model on time series forecast. This mode takes the dynamic correlations among multi variables and environment’s impact on state of equipment into consideration. The proposed approach is applied to the melt channel state forecast. The results are contrasted to MGM model executed on the same test set. The results show the accuracy and promising application of the proposed model. © Springer International Publishing Switzerland 2014.


Wang J.,Tongji University | Kang Q.,Tongji University | Tian H.,Tongji University | Wang L.,Tongji University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This work proposes an efficient charging regulation strategy based on optimal charging priority and location of plug-in electric vehicles (PEVs). It employs a hybrid particle swarm optimization for optimal charging priority and location of PEVs in distribution networks, with the objectives of minimization of charging cost, power loss reduction and voltage profile improvement. The algorithm is executed on IEEE 30-bus test system. The results are compared with those that are gained by executing sample genetic algorithm (SGA) with diverse parameters on the same system. The results indicate the effectiveness and promising application of the proposed methodology. © Springer International Publishing Switzerland 2014.

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