In dna computing and dna nanotechnology, the dnaencoding is one of the most practical and important research topics. dnaencoding need meet simultaneously several physical, chemical and logical constraints, which has...
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In dna computing and dna nanotechnology, the dnaencoding is one of the most practical and important research topics. dnaencoding need meet simultaneously several physical, chemical and logical constraints, which has been proved to be an NP-hard problem. In the paper, a multi-swarm particle swarm optimization is proposed to deal with dnaencodings problem. The method proposed used the local PSO with the time-varying acceleration coefficients (TVAC) as the search engine for each sub-swarms, and incorporated the differential evolution to improve the swarm search space. The results of simulation experiments show that the proposed algorithm is valid and outperforms other evolutionary algorithms.
Quantum evolutionary algorithm (QEA) has been developed rapidly and has been applied widely during the past decade. In this paper, an improved quantum evolutionary algorithm (IQEA) is presented based on particle swarm...
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ISBN:
(纸本)9783642015120
Quantum evolutionary algorithm (QEA) has been developed rapidly and has been applied widely during the past decade. In this paper, an improved quantum evolutionary algorithm (IQEA) is presented based on particle swarm optimization (PSO) and chaos. The simulation results in solving dnaencoding demonstrate that the improved quantum evolutionary algorithm is valid and outperforms the quantum chaotic swarm evolutionary algorithm and conventional evolutionary algorithm. abstract environment.
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