【摘要】 针对多模态优化问题(MultiModal Optimization Problems, MMOPs)的求解,提出了一种基于邻域低密度个体的差分进化算法.该算法在每一代,首先使用密度峰值聚类的方法求得每一个个体的密度,然后,将当前个体邻域范围内密度更低的个体作为变异算子的基向量,随着种群的进化,算法将会自动从探索阶段转化为收敛阶段,进而平衡算法的探索与收敛能力.将提出的算法应用于CEC2013多模态基准测试函数并进行仿真实验,结果表明本文算法在评价指标峰值比和稳定性上与其它基于差分进化的多模态优化算法相比具有明显的优势,并随着测试函数的维度与复杂性的增大,优势就更加明显,其性能优于许多现有的基于差分进化的多模态优化算法.更多还原

【Abstract】 A differential evolution algorithm based on low-density individuals in the neighborhood is proposed to solve MultiModal Optimization Problems(MMOPs). In each generation, the algorithm first relies on density peak clustering to find the density of each individual and then take the lower-density individuals in the neighborhood of the current individual as a base vector of the mutation operator. As the population evolves, the algorithm will automatically transform from the exploration stage to the convergence stage, thereby balancing its exploration and convergence capabilities. The proposed algorithm is applied to the CEC2013 multimodal benchmark function for simulation experiments. Results demonstrate that the algorithm has obvious advantages over other multimodal optimization algorithms based on differential evolution in evaluating the peak ratios and stability of indexes, and the advantage is more distinct with the increasing dimensionality and complexity of the test function. It behaves better than many existing multimodal optimization algorithms based on differential evolution.更多还原