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Simulation Study of Improving Mutative Scale Chaos Optimization Algorithmin Complex Nonlinear System

机译：改进突变尺度混沌优化算法复杂非线性系统的仿真研究

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This paper addresses a reliability optimization problem, where the motive is to select the best optimization method for complex nonlinear system. In order to avoid blind and repeated searching of chaos optimization in searching space of complex nonlinear system, an improving mutative scale chaos optimization algorithm has been proposed to solve the problems. The algorithm counts better value for every searching and sets a sign A in the chaos searching, when the numbers of better value searched is equal to A, the searching space is dynamic reduced according scale, and the above course is repeated in the lesser scale till global optimal value is found. In order to check the reliability of the proposed solution methodology, five complex nonlinear functions have been simulated, the simulation results show that algorithm is simple and local searching ability is better, the efficiency is higher than that of mutative scale chaos optimization, and results demonstrate the benefits of the proposed algorithm for solving this type of problem. Fund Project:Guangdong Province, province, the higher education innovation and strong school project (4724) project funding.
机译：本文涉及可靠性优化问题，其中动机是为复杂非线性系统选择最佳优化方法。为了避免在复杂非线性系统的搜索空间中进行盲目和重复搜索混沌优化，提出了一种改善的突变尺度混沌优化算法来解决问题。该算法对每个搜索的每个搜索来计算更好的值，并在混沌搜索中设置符号A，当搜索的更好值的数量等于A时，按照比例的更好值的数量是动态的，并且以较小的阶段重复上述课程找到全局最佳值。为了检查所提出的解决方案方法的可靠性，已经模拟了五种复杂的非线性功能，仿真结果表明，算法简单，局部搜索能力更好，效率高于突变尺度混沌优化，结果展示提出算法解决这种问题的好处。基金项目：广东省省，高等教育创新和强大的学校项目（4724）项目资金。

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