周期优势结构提炼与搜索路径强化结合提升换热网络结构进化能力

Improvement of Evolutionary Ability of Heat Exchange Network Structure with Periodic Advantage Structure Extraction and Search Path Enhancement

针对强制进化随机游走算法(RWCE)在优化后期会陷入局部最优而使搜索能力下降的问题,提出周期优势结构提炼与搜索路径强化结合策略.首先对系统种群初步优化,每隔一定周期进行一次优势个体提炼,然后采用多重路径复制的方法将这些优势个体给其他个体,最后根据搜索机制遍布整个求解域.以优势个体为中心进行多重路径搜索策略,提高了局部寻优精度,增加了种群多样性,进而增强了全局搜索能力,优化效率和质量得以提高.

To solve the problem that the forced evolutionary random walk algorithm(RWCE)falls into local optimization and reduces search ability in the later stage of optimization,a strategy of combining periodic dominance structure extraction with search path enhancement is proposed.Firstly,population of the system is preliminarily optimized,and the dominant individuals are extracted in certain period.Then these dominant individuals are replicated by multiple paths to other individuals.Finally,according to the search mechanism,they are spread all over the whole solution domain.It shows that the multi-path search strategy centered on dominant individuals improves accuracy of local optimization,increases diversity of population,enhances global search ability,and improves efficiency and quality of optimization.