【作者】 张创业；

【导师】 何登旭；

【作者基本信息】 广西民族大学 ， 计算数学， 2010， 硕士

【摘要】 协同进化是近几年来群智能算法领域兴起的一个研究热点,中外学者都对其进行了广泛的研究。本文在充分研读文献的基础上,对协同进化的效率机制、信息交流方式、拓扑结构及应用领域等方面开展研究,并取得如下成果:(1)提出了新的人工鱼粒子群混合协同优化算法。该算法充分利用了人工鱼群算法与粒子群算法的互补性,发挥了协同进化算法利于混合不同算法的特性。实验表明算法具有收敛速度较快、精度较高的特点;(2)提出了两种新的信息共享拓扑结构:网络信息共享模型和多群多层信息共享模型。这两种模型和以往的模型相比,更能充分将不同搜索机制、不同搜索方法的算法融合于协同进化的机制之中,为问题的求解提供了新思路和新方法;(3)提出了一种新的协同进化信息交流机制——二次协同的信息交流机制。该机制将协同进化的信息交流方式从种群间的信息交流扩展到个体基因组的信息交流,实现了协同信息共享的宏观与微观的结合;(4)扩展了协同进化算法的应用领域。本文将协同进化应用于化工参数估计优化、约束优化、丁烯烷化过程优化、圆填充等问题的求解,为协同进化算法的应用开辟了新的方向更多还原

【Abstract】 Co-evolution is a research focus in the field of swarm intelligence algorithmin recent years, and Chinese and foreign scholars have conducted extensive studieson it. Based on the full research literature, the efficiency mechanisms, andinformation-exchange ways, and topology structure, and applications ofco-evolution, were all studied in this paper, and got some results as follows.(1) A new artificial fish hybridized particle swarm optimization algorithmwas proposed. The new algorithm makes use of the complementarities of artificialfish swarm algorithm and particle swarm optimization algorithm, allows full playto the co-evolutionary algorithm’s characteristics that is beneficial to mix thecharacteristics of different algorithms. The results of experiments showed that thenew algorithm convergence speed and had more precision characteristics;(2) Two new models of information-sharing topology structures, networkinformation sharing model and multi-group multi-layer information sharing model,were proposed. Compared with previous models, these models have moreadvantages in combining the different search mechanisms and the different searchmethods with the mechanisms of co-evolutionary, and they provide a new idea andnew methods for solving problem;(3) The second collaboration of information exchange mechanisms that is anew information exchange mechanisms based on co-evolutionary, was proposed.By turning information exchange only among populations into informationexchange among the individual genome, it brought about the combination of macroand micro in sharing co-evolutionary information;(4) The application field of co-evolutionary algorithm was extended.Co-evolutionary algorithm was applied to chemical parameter estimationoptimization, constrained optimization, butene alkylation processoptimization, circle packing in this paper, and it developed a new directionfor the applications of co-evolutionary algorithm更多还原