分散搜索算法是近年来快速兴起的一种基于种群的进化计算方法,与遗传算法不同的是,它对高质量解和多样性解并存的小数据集使用多种系统子方法和有限次随机过程来获取全局最优解或满意解.基于分散搜索的柔性框架,使用均匀设计来改进以往连续分散搜索算法中的多样性产生方法,将BLX-a算子加以相应改造作为解合并方法,提出了一种基于均匀设计和改造BLX-α算子的新型分散搜索算法(URBSS)来解决非线性连续优化问题.通过8个广为使用的测试函数进行了仿真实验,实验结果表明在与其他连续优化方法的比较中,URBSS能够准确快速地搜索到全局最优解,具有很好的收敛速度和全局优化能力.%Scatter search is a newly emerging population-based evolutionary method that, unlike genetic algorithm, searches for global optima or satisfactory solutions by operating on a small data collection of intensification and diversification, and making much use of various systematic sub-method and limited use of randomization. Furthermore, scatter search uses improvement strategies to efficiently produce the local tuning of the solutions, and an extremely remarkable aspect concerning scatter search is the trade-off between the exploration abilities of the combination method and the exploitation capacity of the improvement mechanism. This paper proposes a novel uniform design and reconstructive BLX-α operator based scatter search algorithm (URBSS) to solve nonlinear continuous global optimizations based on the flexibility of scatter search, which enhances the diversification generation method used in some continuous scatter search before by introducing uniform design, and employs a reconstructive BLX-α operator, that is one of the most effective combination methods for real-coded genetic algorithms, as the solution combination method to form several offspring. Massive simulation experiments are carried out with eight popular-used testing functions, and comparison is performed against some other well-known continuous optimization algorithms presented in the literatures. From the computational results, the URBSS has proved to be quite effective and precise in identifying the global optima, and the global optimization ability and convergence rate are improved.
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