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P systems based multi-objective optimization algorithm
作者姓名:Huang Liang  He Xiongxiong  Wang Ning and Xie Yi
作者单位:National Laboratory of Industrial Control Technology,Institute of Advanced Process Control,Zhejiang University,Hangzhou 310027,China; Zhejiang University of Technology,Hangzhou 310033,China
摘    要:Based on P systems, this paper proposes a new multi-objective optimization algorithm (PMOA). Similar to P systems, PMOA has a cell-like structure. The structure is dynamic and its membranes merge and divide at different stages. The key rule of a membrane is the communication rule which is derived from P systems. Mutation rules are important for the algorithm, which has different ranges of mutation in different membranes. The cooperation of the two rules contributes to the diversity of the population, the conquest of the muhimodality of objective function and the convergence of algorithm. Moreover, the unique structure divides the whole population into several sub populations, which decreases the computational complexity. Almost a dozen popular algorithms are compared using several test problems. Simulation results illustrate that the PMOA has the best performance. Its solutions are closer to the true Pareto-optimal front


P systems based multi-objective optimization algorithm
Huang Liang,He Xiongxiong,Wang Ning and Xie Yi.P systems based multi-objective optimization algorithm[J].Progress in Natural Science,2007,17(4):458-465.
Authors:Huang Liang  He Xiongxiong  Wang Ning  Xie Yi
Abstract:Based on P systems, this paper proposes a new multi-objective optimization algorithm (PMOA). Similar to P systems, PMOA has a cell-like structure. The structure is dynamic and its membranes merge and divide at different stages. The key rule of a membrane is the communication rule which is derived from P systems. Mutation rules are important for the algorithm, which has different ranges of mutation in different membranes. The cooperation of the two rules contributes to the diversity of the population, the conquest of the muhimodality of objective function and the convergence of algorithm. Moreover, the unique structure divides the whole population into several sub populations, which decreases the computational complexity. Almost a dozen popular algorithms are compared using several test problems. Simulation results illustrate that the PMOA has the best performance. Its solutions are closer to the true Pareto-optimal front
Keywords:P systems  evolutionary algorithm  multi-objective optimization  Pareto optimality
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