一种基于多种群对立平行进化策略的DE算法

针对微分进化算法应用于换热网络优化时易陷入局部区域和收敛精度不高的缺点，建立一种多种群对立的平行进化策略的微分进化算法.首先建立原始种群的对立种群；在此基础上，通过原始种群与对立种群的变异操作进行信息共享产生新的试验个体；最后运用多轮对立的思想保持多种群平行进化，使种群在保留当前求解信息的同时又能在求解域内进行更大范围搜索.对换热网络的经典算例计算表明，本文提出的多种群对立平行进化微分进化算法能够有效增强种群多样性，扩大算法的全局搜索能力，跳出局部极值陷阱，得到较好的优化结果.

A Strategy of Differential Evolution with Opposition-based Multi-population Parallel

Generally, differential evolution (DE) algorithm is easily stuck into local optima as well as suffers from low convergence accuracy when employed for optimization of heat exchanger network. To solve these issues, an opposition-based multi-population parallel differential evolution algorithm is proposed. Firstly, opposite population is built by using initial population. Then, new generation of individuals are generated through information exchange, which is produced by mutated operation between opposite population and its original correspondence. The final step is to retain evolution of multi-population in parallel by applying multi-round opposites, so that the population is enable to keep current solution information and search new solutions in a larger space as well. Computing results of improved DE algorithm on 9sp and 15sp suggests that the method improves population diversity, jumps out local optima and at the same time achieves higher speed and accuracy.