基于双向搜索差分进化的多目标优化算法

针对差分进化算法求解多目标优化问题时易陷入局部最优的问题,设计了双向搜索机制以增强DE(Differential Evolution,DE)算法的局部搜索能力。一方面降低了算法陷入局部最优的风险,另一方面可增强Pareto解集的多样性,使Pareto前沿面的解集分布更为均匀。实验结果表明,相比于NSGA-II等同类算法,提出的方法在搜索Pareto最优解时效率更高,并且Pareto最优解集的精度及分布程度比前者更好。

A Multi-objective Optimization Algorithm Based on Differential Evolution with the Bidirectional-search Mechanism

In order to avoid the situation of falling into local optimum when solving the Multi-objective Optimization Problem(MOP) with Differential Evolution Algorithm(DE),we design a bidirectional search mechanism which can improve the ability of local search of the DE and reduce the risk of local optimum,as well as make the Pareto fronts more evenly distributed.Experimental results show that the proposed method is more efficient than similar algorithms such as NSGA-II with better precision and distribution of Pareto optimal solution.