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A planning approach of distributed generation and energy storage based on an improved parallel P-system algorithm
Published in Rodolfo Dufo-López, Jaroslaw Krzywanski, Jai Singh, Emerging Developments in the Power and Energy Industry, 2019
Nan Wang, Han Hao, Yan Cheng, Shumin Sun, Yuejiao Wang
The planning model is a mixed integer nonlinear programming problem, and heuristic algorithms are widespread adopted to solve such optimization problems, including non-dominated sorting genetic algorithm (NSGA) (Deb K. Pratap A. Agarwal S. et al. 2002), the strength Pareto evolutionary algorithm (Mifa Kim, Tomoyuki H. Mitsunori Miki. et a1. 2004), immune algorithm (Gong M. Jiao L. Du H. et al. 2008), particle swarm optimization (Reyes-Sierra, M. & Coello Coello, C. A. 2006) and so on. Although with stable convergence, heuristic algorithms are easy to trap into local optimum, and have the low Pareto-front dispersion and the poor computational efficiency when solving iterative power flow calculation. ‘Membrane computing’, which is also called ‘P system’ proposed by Gheorghe Paun (2000), is a collateral calculation model abstracting from the structure and function of cells, tissues and organs. It is critical to guarantee the diversity and dispersion of Pareto optimal front, and enhance the convergence velocity of iteration in multi-objective optimal planning. Hence, considering the characteristic parallel capacity of P-system, we propose an improved parallel P-system algorithm based on NSGA-II aimed at the improvement of disparity and the convergence of Pareto optimal front.
Membrane computing
Published in International Journal of Parallel, Emergent and Distributed Systems, 2021
Membrane computing, initiated by Prof. Păun [1] in 1998 as a branch of natural computing, is a vigorous computational paradigm motivated by the structure and functioning of the living cells, and from the ways the cells cooperate in populations like tissues, organs, colonies, including neural cells, hence also the brain [2, 3]. The computational models are called either membrane systems or P systems. In the past 20 years, a lot of theoretical results and real-life applications have been achieved in a broad range of topics [4, 5] like computing power, computing efficiency, robots controllers, modelling ecosystems and implementation [6–9]. What is more important, membrane computing community has succeeded to achieve a set of landmarking successes: the establishment of International Membrane Computing Society (IMCS), the organisation of four regular conference/workshop events, namely ECMC, ACMC, BWMC, and CWMC, and the gestation and birth of two periodic publications, Journal of Membrane Computing (JMC) and IMCS Bulletin.
Membrane computing
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Membrane computing, initiated by Prof. Păun [1] in 1998 as a branch of natural computing, is a vigorous computational paradigm motivated by the structure and functioning of the living cells, and from the ways the cells cooperate in populations like tissues, organs, colonies, including neural cells, hence also the brain [2, 3]. The computational models are called either membrane systems or P systems. In the past 20 years, a lot of theoretical results and real-life applications have been achieved in a broad range of topics [4, 5] like computing power, computing efficiency, robots controllers, modelling ecosystems and implementation [6–9]. What is more important, membrane computing community has succeeded to achieve a set of landmarking successes: the establishment of the International Membrane Computing Society (IMCS), the organisation of four regular conference/workshop events, namely ECMC, ACMC, BWMC, and CWMC, and the gestation and birth of two periodic publications, Journal of Membrane Computing (JMC) and IMCS Bulletin.
Simulation of pedestrian behaviours in high-rise buildings based on Intelligence Decision P System
Published in International Journal of Parallel, Emergent and Distributed Systems, 2021
Yunyun Niu, Jieqiong Zhang, Yulin Chen, Jianhua Xiao
Membrane computing is a branch of natural computing, which is inspired by the structure and the function of living cells or the organisation of cells in tissues and organs [19–21]. It provides novel computational models called P systems. Most P systems have been proven to be computationally versatile [22–24]. In addition, P systems have provided non-deterministic frameworks and distributed parallel for computing or optimising [25–27] that have been applied in various aspects of engineering [28–32]. Recently, some membrane systems have involved the concept of position, such as spatial P systems [33] and the grid-exploring P system [34].