<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gexiang Zhang</style></author><author><style face="normal" font="default" size="100%">Miguel A. Gutiérrez-Naranjo</style></author><author><style face="normal" font="default" size="100%">Yanhui Qin</style></author><author><style face="normal" font="default" size="100%">Marian Gheorghe</style></author><author><style face="normal" font="default" size="100%">Ji-Xiang Gheng</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Linqiang Pan</style></author><author><style face="normal" font="default" size="100%">Gheorghe Paun</style></author><author><style face="normal" font="default" size="100%">Tao Song</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Population-Membrane-System-Inspired Evolutionary Algorithm for Distribution Network Reconfiguration</style></title><secondary-title><style face="normal" font="default" size="100%">Asian Conference on Membrane Computing (ACMC 2012)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Pre-Proceedings of Asian Conference on Membrane Computing (ACMC 2012)</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">distribution system reconfiguration</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">membrane-inspired evolutionary algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">population P system</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Huazhong University of Science and Technology</style></publisher><pub-location><style face="normal" font="default" size="100%">Wuhan, China</style></pub-location><pages><style face="normal" font="default" size="100%">139 -160</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper develops a population-membrane-system-inspired
evolutionary algorithm, PMSIEA, which is designed by using a popula-
tion P system and a quantum-inspired evolutionary algorithm (QIEA).
We use a population P system with three cells to organize three types of
QIEAs, where communications between cells are performed at the level
of genes, instead of the level of individuals reported in the existing mem-
brane algorithms in the literature. Knapsack problems are applied to
discuss the parameter setting and to test the effectiveness of PMSIEA.
Experimental results show that PMSIEA is superior to four representa-
tive QIEAs and our previous work with respect to the quality of solutions
and the elapsed time. We also use PMSIEA to solve the optimal distri-
bution system reconfiguration problem in power systems for minimizing
the power loss.
</style></abstract></record></records></xml>